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  • Narrow Banking Explained: Opportunities, Risks and Limits

    Narrow Banking Explained: Opportunities, Risks and Limits

    Narrow banking is an alternative model to traditional banking that separates the role of deposit-taking from the business of lending. In a narrow banking system, deposits are invested only in the safest possible assets such as central bank reserves or short-term government securities. Loans, on the other hand, are made by investment funds or private credit institutions financed with long-term equity or locked-in capital.

    This separation contrasts with the traditional fractional reserve banking system, in which banks use deposits to extend loans while relying on regulatory frameworks and deposit insurance to maintain stability. Narrow banking thus aims to reduce systemic risk by ensuring that deposits are always backed by low-risk assets.

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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.

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  • 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.

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  • 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.

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  • 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.