Do stop-loss orders protect investors?

Author:Brown, Scott
Position:Report
 
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  1. INTRODUCTION

    Throughout 2008 global stock markets lost approximately $29 trillion in value, a fall of 42%. Stop-loss strategies may have played a major role, leading investors to liquidate portfolios with heavy losses. Stop- loss orders played a major role during the 1929 and 1987 crashes, as well as during speculative attacks in foreign exchange markets, see Schiller (1989), and Krugman and Miller (1993), respectively. Bensaid and de Bandt (1998) estimate that around 20% of firms follow a stop-loss strategy in the French Treasury bond futures market.

    Stop-loss strategies are the most commonly used trading strategies in financial practice for portfolio insurance. A stop-loss strategy is intended to limit the downside risk of trading by pre-setting a tolerable loss at the time the investment is made. The simplest stop-loss strategy involves setting a sell level at a fixed percentage below the purchase price at the time of entry. For example, an investor is willing to accept up to a 10% loss on a share of stock bought for $50. The investor's stop-loss level will therefore be set at $45. If the share price falls to this level, $45, the brokerage or clearing firm will automatically sell as per the instructions of the standing stop-order.

    The stop-loss order can be applied to stock, futures, or forex markets. (1) The trailing stop-loss is an order that is placed when the investor enters a position. Stop order levels are also trailed below (above) the market price on long (short) positions. Trailing means that stops are raised (lowered) when the asset price rises (falls) in a long (short) trade, but remain stationary when the price falls (rises). Trailing is done when the market price moves in favor of the trade generating a profit. Acar and Toffel (2000) explain that trailing stops are designed to lock-in profits while allowing for further profit from price momentum. The emphasis here lies in protecting gains and limiting losses as the length of time of holding a position increases.

    The editor of the newsletter supplying our data believes that trading can be enhanced by systematically setting exit conditions ex ante by including stop-losses in the recommendations. Technical trading methods, such as those in Wolf (2002), explain that stop-loss orders are often placed close to a technical level that chartists consider critical for market direction. These might be additional evidences suggesting that technical traders and investors refute the random walk hypothesis and act based on supposed trends and serial correlation in the asset prices they trade.

    The rest of the paper proceeds as follows. Section II describes the existing literature on stop-loss orders. Hypotheses regarding factors that should affect normal returns are developed in Section III. Section IV presents a detailed methodology. The results are presented and discussed in Section V. In Section VI we present and discuss profit/loss tests and regression analysis for all time series under study. Finally, we offer conclusions in Section VII.

  2. PRIOR RESEARCH

    Stop-loss orders are widely used among financial practitioners and touted to increase investment returns. They have been given little attention in the literature which primarily contains studies about limit orders and optimal order selection. In a framework where prices follow a random walk, stop-loss rules would be of little use in active trading. When security returns are unpredictable, selling a losing investment before the end of a holding period does not guarantee that an investor will be better off. Although the investor will not incur any further loss, he or she also gives up the opportunity that the investment may recover during the rest of his holding period. Stop-loss strategies may not be efficient even if security returns are predictable. This is because stop-loss strategies do not incorporate relevant information from the time a strategy is set to the time the contingent sell order is executed. Thus ex-ante stop-loss order rules may fail to incorporate the full set of information available to the investor ex-post. Dybvig (1988) offers evidence against the usefulness of stop-loss orders by showing that such strategies are inefficient relative to other available strategies, such as S&P futures index options.

    However, behavioral arguments have been posited in support of stop-loss strategies. Tschoegl (1988) analyzes stop-loss orders and conjectures that the popularity of these strategies can be derived from a behavioral pattern. The usage of stop-loss orders provides a way for investors to react to shifts in the market, even if they do not know why the shift has occurred. Hence, stop-loss orders provide control limit procedures that shift the monitoring function to the broker, which lower monitoring costs for the investor. Shefrin and Statman (1985) develop a framework called the disposition effect where traders sell winners too early and hold losers too long. They ground their work with prospect theory from Kahneman & Tversky (1979). Subsequent empirical research by Odean (1998) offers support for a disposition effect in the equity markets. The disposition effect arises from behavioral biases such as mental accounting, pride seeking, regret avoidance, and the lack of self-control. A posited remedy for this behavioral tendency is to use stop-loss strategies that force early liquidation of losing investments. Since stop-loss utilizing investors don't have to make contemporaneous selling decisions, such strategies are theorized to prevent behavioral biases.

    The econometric approach to researching stop-loss order strategies has produced mix results. Carr and Jarrow (1990) investigate a particular trading strategy, the so called stop-loss start gain strategy, which includes stop-loss orders. The authors find that with prices following a geometric Brownian motion, the terminal payoffs to a stop-loss strategy is equivalent to a call-option. Osler (2002) examines stop-loss and take-profit orders in the currency markets. He concludes that exchange rate trends are unusually rapid at certain price levels where stop-loss orders are found to cluster in the interbank Fx trading desk order book of the Royal Bank of Scotland. Osler also finds that price volatility associated with stop-loss orders is larger and lasts longer than the response to take-profit orders. These results indicate that stop-loss orders are triggered in waves and contribute to price cascades.

    Macrae (2005) and Ma et al. (2008)T emphasize that the prior literature has failed to address the true effect stop-loss rules have on expected future returns. They illustrate with a few, mainly simulated, examples that stop-loss strategies have hidden costs and, given some assumptions, alter the return distribution in a way not typically expected. In their framework these hidden costs hurt overall performance. To what extent investment performance is affected depends on how the distribution is altered, which depends on how the rule is set.

    Ma et al. (2008), finds that stop-loss orders tend to reduce return volatility, but the opportunity cost of taking losses that subsequently reverse, tends to offset the benefit of avoiding further losses. However, the effectiveness of stop-losses varies with the price drift of the asset. If the asset price is drifting upward (bull market), the use of stop-loss orders reduces the expected return by missing rebounds after minor corrections. On the other hand, if the price is drifting downward (bear market), the use of stop-losses enhance expected returns by avoiding further losses. Similar results hold for profit-taking stops or trailing stop-losses.

    Kaminski and Lo (2008) finds that, under a random walk, simple stop-loss rules always depress expected return. Alternatively, in the presence of momentum and/or regime-switching, stop-loss rules enhance returns. Back tested against monthly returns for broad stock and bond indexes from 1950 through 2004, a simple stop-loss rule outperforms a buy-and-hold strategy by 0.5% to 1.0% per month during stopped-out periods (ignoring trading frictions). The stop-loss rule seems to be able to pick out periods in which long- term bonds substantially outperform equities.

    Lei and Li (2008) examine the impact of stop-loss strategies on the return and risk of individual common stocks. Their results indicate that these strategies neither reduce nor increase investors' losses relative to a buy-and-hold strategy when security returns extend to all possible future paths. One unique stop-loss mechanism, nevertheless, helps investors to reduce investment risk. These findings suggest that the value of stop-loss strategies may come largely from risk reduction rather than return enhancement.

    Erdestam and Stangenberg (2008) examine the efficiency of stop-loss rules by measuring their marginal impact on expected return and risk using volatility as a proxy in comparison with the classic "buy-and- hold' portfolio strategy. They find strong evidence that...

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