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  • U.S. Trade Deficit: Overvalued Dollar vs. Domestic Competitiveness

    U.S. Trade Deficit: Overvalued Dollar vs. Domestic Competitiveness

    The United States has sustained large trade deficits for decades alongside a decline in manufacturing employment. Economists and policymakers debate the root causes of these imbalances. Two prominent explanations have emerged: (1) an overvalued U.S. dollar, which allegedly makes American exports too expensive and imports artificially cheap, and (2) a lack of domestic production competitiveness, meaning structural issues in the U.S. economy (like higher costs or lower productivity) that reduce its ability to compete, regardless of the exchange rate. This report compares and contrasts these arguments – highlighting Stephen Miran’s position in favor of the “overvalued dollar” hypothesis – and reviews critiques from other economists. Key points from both sides are summarized, followed by an assessment of which view is more widely supported in recent expert analyses.

    Argument 1: Overvalued Dollar as the Primary Cause

    Proponents of this view argue that the U.S. dollar’s exchange rate has been persistently and excessively strong, contributing directly to trade deficits and manufacturing challenges. The basic mechanism is that a strong (overvalued) dollar makes U.S. exports more expensive in foreign markets and makes imports into the U.S. cheaper. This price distortion can lead to chronic trade imbalances: Americans buy more foreign goods, while U.S. firms struggle to export. Over time, industries in the U.S. may shrink or offshore due to this competitive disadvantage.

    • Stephen Miran’s Perspective: Economist Stephen Miran contends that the dollar’s unique role as the world’s reserve currency has kept it “persistently overvalued”, placing a heavy burden on U.S. manufacturing . In a recent paper, Miran argues that because other countries demand dollars and U.S. assets (for reserves and investment), the dollar trades above its fair value. He links this to chronic trade deficits and the hollowing-out of American industry. As one summary of Miran’s plan put it: “the US dollar has been overvalued for decades, leading to chronic trade deficits — and the migration of manufacturing out of the United States” . In Miran’s telling, the high dollar benefits financial sectors and consumers (through cheaper imports) but “weighed heavily on the American manufacturing sector” by undercutting export competitiveness .

    • Reserve Currency and Capital Inflows: Miran (and others with similar views) often invoke the “Triffin dilemma” – the idea that issuing the world’s reserve currency forces the U.S. to run international deficits. Foreign governments and investors park savings in dollar assets (like U.S. Treasury bonds), which boosts demand for dollars and drives up its value . According to this view, the U.S. ends up importing capital and, as a mirror image, running trade deficits not because Americans are uncompetitive per se, but because the world’s demand for dollars prices U.S. goods out of global markets. Miran writes that the U.S. “runs large current account deficits not because it imports too much, but it imports too much because it must export [U.S. Treasuries] to provide reserve assets” . In other words, the trade deficit is seen as a consequence of the dollar’s international role and resultant overvaluation.

    • Evidence Cited: Advocates of this argument point to historical episodes and data. For example, economist Richard Koo notes that U.S. trade deficits widened in the late 1990s even when the federal budget was in surplus, because the dollar sharply appreciated during that period. The strong dollar in the late 1990s and early 2000s coincided with many U.S. manufacturers (e.g. in electronics and appliances) going bankrupt as they “could not compete with imports given such a strong dollar”. Research supports a linkage between a high dollar and manufacturing decline – one study found that the dollar’s 33% surge from 1995–2002 caused a roughly 4% drop in U.S. manufacturing employment (about 740,000 jobs lost) beyond what would have occurred due to productivity gains or recession alone. In short, the claim is that an overvalued currency eroded the competitiveness of U.S. producers, contributing significantly to factory job losses and import penetration.

    • Policy Prescriptions: If the overvalued dollar is the culprit, the implied solution is to bring the dollar to a more competitive level. Miran and like-minded economists have floated ideas such as coordinated currency interventions (a modern-day Plaza Accord) or shifts in U.S. policy to deemphasize the “strong dollar” stance . Miran’s paper discusses a potential “Mar-a-Lago Accord” where U.S. trading partners would cooperate to weaken the dollar’s value . The goal of such measures would be to boost U.S. export competitiveness, reduce imports, and thereby narrow the trade deficit while revitalizing domestic manufacturing. These proponents often argue that exchange rate adjustments are a more effective and less risky tool than tariffs for rebalancing trade . In summary, the overvalued-dollar camp believes currency misalignment is the primary distortion that needs correction to fix trade and manufacturing woes.

    Argument 2: Lack of Domestic Competitiveness as the Primary Cause

    The opposing viewpoint holds that America’s trade deficits and manufacturing challenges are rooted more in domestic economic factors and competitiveness issues than in currency values. Those who subscribe to this explanation acknowledge that the dollar’s value matters at the margin, but they see deeper structural reasons for the U.S. trade imbalance – such as higher relative production costs, insufficient savings, or weaknesses in industrial strategy. In their view, focusing on the dollar overstates the case and risks ignoring the real issues that make U.S. firms less competitive.

    • Higher Costs and Lower Productivity: A common argument is that U.S. manufacturing’s decline is largely due to factors like wage differentials, productivity trends, and automation, rather than just an expensive dollar. As trade expert William Reinsch explains, “Most economists today argue that while exchange rates are a factor in manufacturing job losses, the larger causes are higher wages (in the U.S. versus developing countries), low productivity growth, and technology improvements”. Many U.S. industries that left over the past half-century did so because other countries offered cheaper labor or more efficient production for certain goods. Even during periods when the dollar was weaker, the U.S. still ran trade deficits and lost manufacturing jobs, indicating currency is not the sole driver. The United States long ago transitioned away from being a low-wage manufacturing economy, so many lost industries (e.g. consumer electronics, textiles) would not return even with a cheaper dollar. In short, this side argues that American producers often struggle due to structural cost disadvantages and technological changes that have reduced labor demand, which can’t be solved simply by tweaking exchange rates.

    • Saving-Investment Imbalances: Another lens for the competitiveness argument is macroeconomic: the idea that trade deficits reflect underlying imbalances in saving and investment, not manipulation of currency. In basic terms, the U.S. trade deficit equals the net inflow of foreign capital – Americans buy more from abroad than they sell abroad because the U.S. economy absorbs more investment and spending than it generates in savings. From this perspective, the cause of the trade deficit is that the U.S. consumes or invests beyond its means (financed by foreign capital), rather than a currency aberration. As Reinsch summarizes the standard view, trade imbalances are often seen as “a reflection of which country is investing more and which is saving more. Americans are simply investing and spending more than they save”, with foreigners filling the gap. This view implies that to reduce the trade deficit, the U.S. would need to save more or spend less, which can be painful (potentially slowing growth). In practical terms, policies like large budget deficits can widen the trade gap – indeed, former IMF economist Maurice Obstfeld notes that higher U.S. fiscal deficits will likely raise trade deficits, all else equal . The emphasis here is that the trade deficit is anchored in macroeconomic choices and fundamentals (fiscal policy, household saving rates, investment climate), not just the dollar’s reserve status. In fact, Obstfeld’s recent analysis finds the U.S. “can supply the world with dollars without [running] trade deficits”, and he argues the notion that the dollar’s reserve role “obliges” the U.S. to have deficits is a myth. This directly challenges Miran’s thesis – suggesting the dollar’s status is not an excuse for perpetual deficits, and domestic policies can adjust the saving-investment balance.

    • Critiques of the Overvalued-Dollar Thesis: Economists who favor the competitiveness explanation have critiqued Miran’s position as overly simplistic. Raghuram Rajan, for example, flatly describes Miran’s strong-dollar-causes-deficits argument as “not a persuasive argument”. He and others contend that Miran essentially inverts cause and effect – implying the U.S. is a passive victim of foreign capital inflows – whereas in reality those inflows also reflect U.S. economic attractiveness (e.g. investors seeking safe U.S. assets) and America’s own demand for capital. In other words, the U.S. trade deficit is at least partly self-inflicted, stemming from domestic demand outpacing supply. Critics also note that blaming the dollar can downplay the need for the U.S. to improve its productivity or industrial strategy. Even if the dollar were devalued, would American manufacturing rebound if, say, factories are highly automated or if skills and supply chains have atrophied? Reinsch points out that many industries “will not return regardless of the value of the dollar” due to structural reasons. Moreover, some economists worry that attempts to force a weaker dollar (for example, by undermining the dollar’s reserve-currency status) could backfire – raising U.S. interest rates, deterring investment, or eroding the very advantages (like lower borrowing costs) that the U.S. gets from issuing the world’s preferred currency. A recent analysis by Obstfeld concludes that trade balances are “nuanced” outcomes of both foreign and domestic factors, but often “U.S. factors are dominant” – for instance, he notes that U.S. budget deficits and China’s high saving rates have played big roles in global imbalances. From this vantage, the remedy for trade deficits lies in addressing those underlying factors (e.g. reducing fiscal deficits, encouraging investment in productive capacity, workforce skills, etc.) rather than primarily in exchange rate engineering.

    • Acknowledging Exceptions: Importantly, even those stressing competitiveness admit that currency can matter in certain cases. A clear example was China in the 2000s – when China maintained an undervalued yuan and ran huge surpluses, contributing to a surge of Chinese imports that harmed some U.S. industries. This period saw a hot debate over China’s currency policy and its impact on U.S. jobs. While “a strong case can be made that China was [guilty of currency manipulation] then”, Reinsch notes that “most recent data suggests China, if anything, is now trying to prop its currency up rather than force it down”. In other words, currency misalignment has at times exacerbated competitiveness problems (as with China’s peg in the early 2000s), but such misalignment is not universally the main story behind U.S. trade deficits in the postwar era. Over the last 50 years, the U.S. ran trade deficits through periods of both strong and weak dollar values, implying broader forces at work. This reinforces the view that while the dollar’s value is one piece of the puzzle, the primary drivers of the trade deficit lie in economic fundamentals and policy choices rather than an inherently overvalued currency.

    Side-by-Side Comparison of the Two Views

    To clarify the differences between these arguments, the table below compares key points of the “Overvalued Dollar” hypothesis and the “Lack of Competitiveness” hypothesis:

    Aspect“Overvalued Dollar” Explanation“Lack of Competitiveness” Explanation
    Core ClaimThe U.S. dollar’s exchange rate is too high (overvalued), primarily due to global demand for dollar assets (reserve currency status). This makes U.S. goods expensive abroad and imports cheap at home, causing persistent trade deficits and industrial decline .The U.S. trade deficit reflects domestic economic weaknesses or choices – e.g. higher production costs, lower competitiveness, or Americans consuming more than they produce. U.S. manufacturing job losses are mainly due to wage and productivity gaps and technological change, not just currency levels .
    Key ProponentsStephen Miran (former Trump economic adviser) is a leading voice, arguing the dollar’s reserve role has “weighed heavily” on U.S. manufacturing . Others like economist Richard Koo and some U.S. manufacturers echo that a strong dollar unfairly handicaps industry . “Weak-dollar” advocates often call for a new Plaza Accord to realign currencies.Many mainstream economists and institutions. Critics of the currency view include Raghuram Rajan (former IMF chief) who finds it unconvincing , Maurice Obstfeld (PIIE/Berkeley) who labels the reserve-currency/deficit link a “myth” , and trade analysts like William Reinsch . They represent a broad consensus that macro and structural factors drive the deficit.
    Evidence CitedHistorical periods of a strong dollar correlate with widening trade gaps and factory closures. E.g., late 1990s: the dollar surged and the trade deficit doubled even as the US had budget surpluses . Studies estimate that the 1995–2002 dollar rise alone cost hundreds of thousands of U.S. manufacturing jobs . The last major effort to weaken the dollar (1985 Plaza Accord) successfully cut the dollar’s value in half and was followed by a shrinking trade deficit, which proponents cite as a positive precedent .Long-term trends show trade deficits persisting regardless of dollar fluctuations. Over 50+ years, the U.S. has run deficits even when the dollar was relatively weak . U.S. manufacturing decline aligns with the rise of low-wage competitors (China, Mexico, etc.) and automation. For instance, the “China shock” of the 2000s hurt U.S. jobs largely due to China’s industrial ascent (enabled partly by a then-undervalued yuan) . Also, episodes like 1998–2001 demonstrate that factors like domestic demand booms or financial flows (the late-90s stock market bubble) can drive trade deficits even in the absence of fiscal deficits .
    Policy ImplicationsWeaken the dollar to restore balance. Options include coordinated interventions or agreements with trading partners to depreciate the dollar , or unilateral measures to discourage foreign dollar accumulation. Miran suggests the U.S. could negotiate sharing the “burden” of the strong dollar by capturing some benefits from other countries that currently enjoy cheap exports . Tariffs can be used tactically, but only if the dollar’s value is managed in parallel (since tariffs alone may be offset by currency moves) . Overall, this camp prioritizes currency realignment as the solution to boost exports and revive manufacturing.Improve competitiveness and rebalance macroeconomics. Recommended responses include investing in productivity and skills, fostering high-value industries, and addressing macro imbalances. For example, reducing federal budget deficits (to increase national saving) would, over time, help lower the trade deficit . Policies might also focus on fair trade enforcement (e.g. tackling foreign subsidies or IP theft) and workforce development, rather than attempting to micromanage the dollar’s value. This side sees a weaker dollar as at best a partial fix – helpful for exports, but not sufficient unless underlying cost/productivity issues are solved.

    Which View Do Economists Support Today?

    While the debate continues, recent expert analysis leans strongly toward the “domestic competitiveness (and macro factors)” explanation as the dominant cause of U.S. trade deficits. Stephen Miran’s overvalued-dollar argument has influenced some policy discussions – especially in the context of former President Trump’s trade agenda – but it has faced skepticism from many economists. In the words of Raghuram Rajan, Miran’s claim that America “runs large trade deficits and struggles to compete in manufacturing because [the] dollar [is] too strong… is not a persuasive argument” . Rajan and others emphasize that attributing the entire problem to the dollar’s reserve-currency status is an oversimplification.

    Multiple economists have published critiques of the overvalued-dollar thesis in the past year: for example, Maurice Obstfeld (2025) directly addressed the notion that the dollar’s reserve role forces U.S. deficits, calling this a “myth” and showing that domestic fiscal and savings decisions are more important. Likewise, William Reinsch (2024) concurs that exchange rates alone cannot explain decades-long deficits, pointing instead to wage and productivity differentials and the long-running shift of production to lower-cost countries. This mainstream consensus holds that the U.S. trade deficit is largely self-determined – driven by how much Americans spend and invest relative to their income, and by the competitiveness of U.S. firms – rather than being an externally imposed byproduct of the dollar’s popularity. In practical terms, this means experts often recommend structural and macroeconomic fixes (like boosting innovation, education, and fiscal discipline) to address trade imbalances, rather than primarily tinkering with exchange rates.

    That said, the overvalued-dollar argument is not without its supporters. It resonates in manufacturing circles and among policy strategists who observe that a markedly strong dollar does hurt export industries. Even some economists who focus on trade fairness agree that a gradual dollar depreciation could help U.S. industry in the short run. The difference is that most do not see it as a silver bullet. The prevailing view is that currency values are one piece of a larger puzzle: a persistently high dollar can exacerbate trade deficits, but fundamental competitiveness and macroeconomic balance ultimately determine the long-run outcome .

    Conclusion

    In summary, two contrasting explanations exist for America’s trade deficit and manufacturing travails. Stephen Miran and other “weak-dollar” proponents make a compelling case that an overvalued dollar – bolstered by global reserve demand – has been a major headwind for U.S. exporters and a key driver of the trade gap. They call for policies to realign the dollar to more competitive levels. On the other side, most economists argue the trade deficit primarily reflects homegrown economic factors: the U.S. needs to produce more competitively and get its financial house in order. They caution that focusing solely on the dollar could distract from tackling deeper issues like productivity, skills, and saving rates.

    Recent expert analyses and the weight of economic research tend to support the latter view, that lack of competitiveness and macro imbalances are the root causes of U.S. trade deficits. The “overvalued dollar” is seen more as a symptom or contributing factor than the sole cause. As Obstfeld notes, foreign and domestic policies can affect trade flows, but they are “not principal drivers” of the overall deficit imbalance . Ultimately, a balanced approach recognizing both perspectives may be prudent: addressing legitimate concerns about dollar misalignment (especially when due to other countries’ actions or market distortions) while also strengthening the underlying competitiveness of U.S. production. This combination would ensure that any exchange rate adjustments translate into sustainable gains for American manufacturing and a healthier trade position in the long run.

    Sources:

    • Miran, Stephen. “A User’s Guide to Restructuring the Global Trading System.” Hudson Bay Capital research paper (2020) – argues the dollar’s reserve status causes overvaluation and U.S. trade imbalances .

    • Rajan, Raghuram G. “Trumponomics’ Exorbitant Burden.” Project Syndicate (Mar 11, 2025) – critiques Miran’s argument as unconvincing .

    • Obstfeld, Maurice. “The U.S. Trade Deficit: Myths and Realities.” Brookings Papers on Economic Activity (Mar 2025) – finds trade deficits stem from macro factors (e.g. low savings, fiscal deficits) and calls the reserve-dollar deficit link a “myth” .

    • Reinsch, William Alan. “Putting the Dollar in Perspective.” CSIS Commentary – Back & Forth: Dollar Valuation (Oct 2024) – argues higher labor costs, slow productivity, and technology are bigger causes of U.S. manufacturing loss than currency value .

    • Koo, Richard. “The Dollar and Social Divisions in the United States.” CSIS Commentary – Back & Forth: Dollar Valuation (Oct 2024) – presents the case that an overvalued dollar (due to free capital flows) has undermined U.S. industry, analogous to Miran’s view .

    • Bloomberg News. “Dollar Drop Clouds Debate on Who Pays for Tariffs.” Bloomberg (Sept 2019) – notes manufacturers often complain a strong dollar makes U.S. exports less competitive .

    • Economic Policy Institute. “The High Dollar’s Effect on U.S. Manufacturing.” EPI Report (2003) – empirical study quantifying job losses from the late-1990s dollar rise .

  • The Importance of Limiting Negative Performance for Compounding in Stock Market Investments

    The Importance of Limiting Negative Performance for Compounding in Stock Market Investments

    When investing in the stock market, it is easy to get caught up in the allure of high returns during booming markets. However, for investors with a long-term horizon, the real secret to building wealth isn’t just about capturing the highs – it’s about avoiding the lows. The impact of negative years on your investment portfolio can be far more detrimental than the gains achieved during positive years, especially when compounding over a long period of time. This article explores why limiting negative performance is more critical than chasing high returns and how this strategy can lead to more consistent and substantial growth over time.

    The Impact of Compounding

    To understand why limiting negative performance is so crucial, it’s important to grasp the concept of compounding. Compounding refers to the process where the value of an investment grows because the earnings on an investment, both from capital gains and interest, earn interest as time passes. Essentially, it’s earning returns on your returns, which accelerates the growth of your portfolio over time.

    However, compounding works both ways. Just as your gains can multiply, so can your losses. A significant loss in one year can wipe out the gains made in several previous years, making it harder for your portfolio to recover. For example, if your portfolio loses 50% in one year, you need a 100% gain the following year just to break even.

    The Case of the S&P 500: 2000-2023

    Let’s examine the performance of the S&P 500 from 2000 to 2023 to illustrate this point. During this period, the S&P 500 experienced several years of negative returns, including a devastating -37% in 2008 during the financial crisis. Despite these setbacks, the average yearly return for the S&P 500 over this period was approximately 7.63%. However, the Compound Annual Growth Rate (CAGR) – a more accurate reflection of the investment’s true growth -was only 6.06%. This difference highlights the negative impact of years with losses on long-term growth.

    To explore the impact of avoiding negative returns, let’s consider a hypothetical scenario where each year with a negative return in the S&P 500 was limited to no return (0%). Under this scenario, the CAGR for the same period would have increased dramatically to approximately 11.47%. This example underscores how even modest reductions in losses during bad years can significantly boost long-term growth, far outweighing the benefits of capturing every bit of upside in good years.

    Why Avoiding Losses Matters More Than Chasing Gains

    1. The Mathematics of Losses: Losses have a disproportionate effect on your portfolio. A 50% loss requires a 100% gain to recover, while a 20% loss requires a 25% gain. The larger the loss, the harder it is to get back to the original value, making it critical to avoid large drawdowns.
    2. Volatility Drag: The fluctuation in returns, known as volatility, can reduce your overall returns through a phenomenon called volatility drag. Even if your average return is positive, high volatility can lead to a lower compounded return over time, as seen in the difference between the average return and CAGR for the S&P 500.
    3. Psychological Impact: Sustained losses or a significant market downturn can lead to panic selling, where investors sell off their holdings to avoid further losses. This behavior can lock in losses and prevent investors from benefiting from a market recovery. By reducing exposure to negative years, investors are more likely to stay the course and benefit from long-term compounding.

    Strategies to Limit Negative Performance

    1. Diversification: One of the most effective ways to mitigate losses is through diversification. By spreading investments across different asset classes, sectors, and geographies, you reduce the impact of a downturn in any single area.
    2. Risk Management: Implementing risk management strategies such as stop-loss orders, portfolio rebalancing, and the use of hedging instruments can help protect against significant losses during market downturns.
    3. Focus on Quality: Investing in high-quality companies with strong balance sheets and stable earnings can provide more resilience during market downturns, limiting the extent of losses during bad years.
    4. Long-Term Perspective: Maintaining a long-term perspective allows investors to avoid panic selling during downturns and stay focused on the bigger picture. Over time, the market tends to recover from losses, but only if you remain invested.

    Conclusion

    In the quest for long-term wealth building, it’s not the years of high performance that will make or break your investment portfolio – it’s the years of significant losses. By focusing on strategies that limit negative performance, you can enhance the power of compounding and achieve more consistent growth over time. The S&P 500’s performance from 2000 to 2023 clearly illustrates that avoiding the lows is often more important than capturing the highs. For long-term investors, this approach can lead to more substantial and reliable returns, helping to achieve financial goals with greater certainty.

  • The Moving Average Convergence Divergence (MACD)

    The Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. The result of this subtraction is known as the MACD line. Additionally, a “signal line” is calculated, which is the 9-period EMA of the MACD line itself. The MACD indicator helps traders understand whether the bullish or bearish movement in the price is strengthening or weakening.

    Formula for Calculating MACD

    The MACD is comprised of three components:

    1. MACD Line: The difference between the 12-period EMA and the 26-period EMA. [ \text{MACD Line} = \text{EMA}{12} – \text{EMA}{26} ]
    2. Signal Line: The 9-period EMA of the MACD Line. [ \text{Signal Line} = \text{EMA}_{9}(\text{MACD Line}) ]
    3. Histogram: The difference between the MACD Line and the Signal Line, often plotted as a bar chart around the zero line. [ \text{Histogram} = \text{MACD Line} – \text{Signal Line} ]

    Python Code for MACD Calculation

    To calculate the MACD and its signal line in Python, you can follow this approach, assuming you have a list of prices and functions to calculate the EMA:

    def calculate_ema(prices, period, smoothing=2):
    ema = [sum(prices[:period]) / period]  # Initial EMA using SMA
    multiplier = smoothing / (1 + period)
    for price in prices[period:]:
        ema.append((price - ema[-1]) * multiplier + ema[-1])
    return ema

def calculate_macd(prices):
    ema12 = calculate_ema(prices, 12)
    ema26 = calculate_ema(prices, 26)
    macd_line = [ema12_val - ema26_val for ema12_val, ema26_val in zip(ema12[-len(ema26):], ema26)]
    signal_line = calculate_ema(macd_line, 9)
    histogram = [m - s for m, s in zip(macd_line[-len(signal_line):], signal_line)]
    return macd_line, signal_line, histogram

    Interpretation and Usage in Swing Trading

    In swing trading, the MACD is used to identify momentum and potential reversals in the market:

    • Bullish Signals: When the MACD line crosses above the signal line, it suggests an increasing bullish momentum, and traders might consider entering a long position.
    • Bearish Signals: Conversely, when the MACD line crosses below the signal line, it indicates growing bearish momentum, and traders might consider selling or entering a short position.
    • Divergence: If the MACD is moving away from the trend shown by the price action (e.g., the price is making new highs, but the MACD is not), it can indicate a weakening of the current trend and a possible reversal.

    The MACD histogram provides further insight into the momentum and potential reversals. A histogram above zero suggests bullish momentum, while below zero can indicate bearish momentum. When the histogram starts to decline towards the zero line, it indicates that the current trend is weakening.

    The MACD’s effectiveness can vary across different markets and timeframes. Traders often use it in combination with other indicators and analysis techniques to confirm potential trading signals and improve their decision-making process.

  • Why short selling is not the opposite of going long

    Short selling and going long are two fundamental investment strategies with distinct risk profiles and market expectations. While going long involves buying assets with the expectation that their value will rise over time, short selling is the practice of borrowing assets to sell them at current prices, hoping to buy them back later at a lower price, profiting from the difference. The key differences, particularly the asymmetric risk profile and market tendencies, can be better understood through the lens of financial experts and the insights of Nassim Nicholas Taleb.

    Asymmetric Risk Profile

    1. Going Long: Limited Loss, Unlimited Gain – When you buy (go long on) a stock, the maximum you can lose is what you have invested, as a stock’s price cannot go below zero. However, the potential for gain is theoretically unlimited, as there is no cap on how high a stock’s price can rise.
    2. Short Selling: Unlimited Loss, Limited Gain – In contrast, short selling exposes you to potentially unlimited losses because there’s no upper limit to how high a stock’s price can go. However, the maximum gain is limited to the initial value from which the stock is shorted, minus the cost to buy it back, as a stock’s price cannot fall below zero.

    This asymmetric risk profile is crucial because it reflects the fundamental difference in risk exposure. Nassim Nicholas Taleb, in his discussions on risk, probability, and unpredictability in markets, often emphasizes the importance of managing tail risks – rare and extreme events that can have disproportionately large impacts. Short sellers are particularly exposed to these tail risks, as unforeseen positive news or market shifts can lead to significant losses.

    Markets’ Tendency to Rise Over Time

    Historical data shows that over the long term, markets tend to go up. This upward bias is attributed to economic growth, inflation, and reinvestment of dividends, among other factors. This tendency means that going long generally aligns with the overall direction of market movement, offering a tailwind to investors.

    On the other hand, short sellers bet against this general trend, which can make short selling a more challenging and risky strategy over the long term. This is not to say that short selling cannot be profitable, but it requires accurate timing and often a contrarian view that a particular stock or the market will decline. Financial experts and economists often point out that while short selling can be a useful hedge against market downturns or for arbitrage, it is a strategy fraught with risks, especially considering market efficiency and the difficulty of timing market movements accurately.

    Conclusion

    In essence, while going long and short selling are both strategies used to seek profit in the markets, their risk profiles are fundamentally different due to the asymmetric nature of potential gains and losses and the general upward trend of markets over time. Short selling, while potentially profitable, requires careful management of risks, especially those associated with rare but extreme market movements that various financial experts warn against. Investors must carefully consider these dynamics and their own risk tolerance when choosing between these strategies.

    For further detailed analysis and insights, consulting specific sections of Nassim Nicholas Taleb’s writings on risk and unpredictability, as well as financial literature on investment strategies, would provide deeper understanding and context.

  • Use of different SMA and EMA periods in Swing Trading

    The choice of different periods for the Exponential Moving Average (EMA) and the Simple Moving Average (SMA) in technical analysis reflects the varying needs and strategies of traders and investors, as well as the distinct characteristics of these two types of moving averages. Each moving average type and its associated period settings serve specific purposes, catering to different trading styles, objectives, and sensitivities to market movements. Here’s a breakdown of why different periods are used for EMA and SMA:

    Responsiveness to Price Changes

    • EMA: The EMA gives more weight to recent prices, making it more responsive to new information and price changes. This makes shorter EMA periods particularly useful for traders looking to capitalize on short-term trends and react quickly to market movements. The use of different periods allows traders to fine-tune their analysis to match their trading frequency and to capture trends as they develop.
    • SMA: The SMA provides an equal weighting to all prices in the period, resulting in a smoother and less responsive curve compared to the EMA. Longer periods for the SMA are often used to identify more established trends and to filter out short-term market noise. This can be beneficial for longer-term investors or traders looking for more significant trend reversals or support and resistance levels.

    Trading Strategies

    • Short-Term Trading: Traders focused on short-term strategies may prefer shorter EMA periods because they can provide early signals for entering and exiting trades. The responsiveness of the EMA to recent price movements makes it suitable for this trading style.
    • Long-Term Investing: Investors with a long-term horizon may lean towards using longer SMA periods. The SMA’s smoothing effect can help identify long-term trends and reduce the impact of short-term volatility, providing a clearer picture of the underlying trend direction.

    Analysis Objectives

    • Trend Confirmation: Different periods can help confirm trends over various timeframes. For instance, a long-term investor might use a 200-day SMA to confirm a major trend, while a swing trader might look at a 50-day EMA for medium-term trend confirmation.
    • Signal Generation: The use of two moving averages of different lengths (one shorter, one longer) is common in crossover strategies. For example, a 12-day EMA crossing above a 26-day EMA might be used as a buy signal, reflecting a shift in short-term momentum relative to the medium-term trend.

    Asset Characteristics

    • Volatility: More volatile assets might require shorter periods to more accurately reflect recent price movements, while less volatile assets can be analyzed with longer periods without sacrificing timeliness.
    • Market Conditions: During periods of high market volatility, traders might adjust the periods of EMAs or SMAs to reduce noise or to capture more significant trends.

    In summary, the choice between different periods for EMA and SMA, and between EMA and SMA themselves, depends on the trader’s or investor’s goals, the nature of the asset being analyzed, and the market context. Adjusting the periods allows analysts to tailor their approach to fit their analysis needs, risk tolerance, and trading or investment strategy.

  • The Exponential Moving Average (EMA)

    The Exponential Moving Average (EMA) is another popular technical analysis indicator used to identify trends by smoothing out price data, similar to the Simple Moving Average (SMA). However, the EMA gives more weight to recent prices, making it more responsive to new information compared to the SMA. This characteristic makes the EMA a preferred choice for many traders, especially those looking to catch trends early.

    Formula for Calculating EMA

    The formula for calculating the EMA involves several steps, with the most critical being the application of a multiplier to give more weight to recent prices. The EMA for a given period (N) is calculated as:

    [ \text{EMA}{\text{today}} = \left( \text{Price}{\text{today}} \times \frac{2}{N + 1} \right) + \text{EMA}_{\text{yesterday}} \times \left( 1 – \frac{2}{N + 1} \right) ]

    Where:

    • Price(_{\text{today}}) is the closing price for the current period.
    • (N) is the number of periods.
    • EMA(_{\text{yesterday}}) is the EMA value from the previous period.
    • (\frac{2}{N + 1}) is the weighting multiplier applied to the most recent price.

    The initial EMA value is typically calculated using the SMA of the initial (N) periods as a starting point.

    Python Code for EMA Calculation

    Calculating the EMA in Python requires keeping track of the EMA value across periods. Here’s a simplified approach to calculate the EMA for a series of prices:

    def calculate_ema(prices, period, smoothing=2):
    ema = [sum(prices[:period]) / period]  # Start with SMA for the first period
    multiplier = smoothing / (1 + period)
    for price in prices[period:]:
        ema.append((price - ema[-1]) * multiplier + ema[-1])
    return ema

    This function starts by calculating the initial EMA using the SMA for the first period days. It then calculates subsequent EMA values using the formula provided, with the smoothing factor set to 2 by default, which is a common choice.

    Most Common Periods for EMA in Swing Trading

    Swing traders use various EMA periods to identify trading opportunities based on short- to medium-term trends. Common EMA periods include:

    • 12-day EMA: Useful for short-term trend analysis and often paired with the 26-day EMA to create the Moving Average Convergence Divergence (MACD) indicator.
    • 26-day EMA: Often used in conjunction with the 12-day EMA for signals when the two cross over.
    • 50-day EMA: Provides a medium-term outlook and is used to gauge the direction of the mid-term trend. Prices above this EMA are often considered bullish, while prices below can indicate a bearish trend.
    • 200-day EMA: While more common in long-term trend analysis, it can also serve as a benchmark for the overall market trend in swing trading strategies.

    The choice of period depends on the trader’s strategy, the market conditions, and the specific characteristics of the asset being traded. The responsiveness of the EMA makes it particularly useful for identifying trend directions more quickly than the SMA, which can be beneficial in swing trading scenarios where catching trends early is crucial.

  • The Simple Moving Average (SMA)

    The Simple Moving Average (SMA) is a widely used indicator in technical analysis that helps smooth out price data by creating a constantly updated average price. The SMA is calculated by adding together the prices of a security or currency over a specific number of periods and then dividing this total by the number of periods. This process produces a smooth line that traders can use to identify the direction of a trend or to determine support and resistance levels.

    Formula for Calculating SMA

    The formula for calculating the SMA is straightforward. For a given period (N), the SMA is calculated as:

    [ \text{SMA} = \frac{\text{Sum of Prices over last } N \text{ periods}}{N} ]

    Where:

    • Sum of Prices over last (N) periods is the total of the closing prices (or another price point, though closing prices are most common) of the asset for the (N) periods.
    • (N) is the number of periods.

    Python Code for SMA Calculation

    Here is a simple Python function to calculate the SMA given a list of prices and a period:

    def calculate_sma(prices, period):
    if len(prices) < period:
        return None  # Not enough data to calculate SMA
    return sum(prices[-period:]) / period

    This function takes a list of prices (prices) and a period (period) as arguments. It calculates the SMA based on the most recent period prices in the list. If there aren’t enough prices in the list to match the period specified, it returns None.

    Most Common Periods for SMA in Swing Trading

    In swing trading, which typically involves holding positions from several days to several weeks, traders often use specific periods for the SMA to help identify medium-term trends and potential reversal points. The most common periods for SMA in swing trading include:

    • 10-day SMA: This short-term average can help identify quick trend shifts and is often used for more aggressive swing trading strategies.
    • 20-day SMA: Considered a good indicator of the short to medium-term trend. Crossing above the 20-day SMA might be seen as a bullish sign, while crossing below it might be seen as bearish.
    • 50-day SMA: This is a widely watched medium-term trend indicator, often used to assess the health of a trend. Many traders view prices above the 50-day SMA as being in a bullish trend, and prices below it as being in a bearish trend.
    • 200-day SMA: Although more commonly associated with long-term trend analysis, some swing traders might use the 200-day SMA to gauge the overall market sentiment and to identify major trend reversals.

    Each of these periods can be adjusted based on the trader’s strategy, the asset being traded, and market volatility. Traders often experiment with different periods to find the ones that best suit their trading style and objectives.