The risks and rewards of short term trading entice a small unique community of traders, while averting others. The short term strategy for trading stocks is zero-sum methodology that has the potential to invoke early retirement or staggering, systematic economic loss. However, (Diether, Lee & Werner, 2009) supported that short term traders positively contribute to the markets through corrections to short-term stock price deviations from the fundamental values. Short term trading is at the root of Keynes’ perspective of the financial markets. This paper will present a qualitative review of short term trading; short term trading strategy theory; and how short term trades impact the market.
Short term trading has become of particular interest to issuers, academic institutions, the Securities and Exchange Commission (SEC), and representatives of the media. Following economic crises, investors often return to the drawing board to reassess stock market conditions and existing market theory. Short term investors follow positive returns and predict future negative abnormal returns based upon historical returns. Long term investors base the time that positions are held upon profitability and market volatility.
The selection of stocks varies between short and long term traders, based upon a number of market indicators. Both short and long term traders prefer high turnover stocks; however, short term traders exhibit a significantly stronger preference for the turnover. (Saglam, Moallemi & Sotiropoulos, 2016) presented that sophisticated short term traders accurately predict the short term future runs; which results in the appearance of inflated costs of trade in juxtaposition to trades by benchmark noise traders. Legendary short term trader, Wyckoff presented that individuals who have the capacity to interpret which transactions occur by the minute, or by the second have distinct advantages over the general population of traders.
Short term traders time trades accurately against the short-term price trends. (Cespa & Vives, 2014) presented that market prices reflect the investor’s average expectations in regard to the basic information and liquidity trades. Stochastic and RSI indicators are often used by stock traders to identify market conditions that are considered overbought or oversold. (Scott, 2016) proposed short term trading strategies that use the RSI indicator to measure the velocity of stock price movement; and more specifically, to identify stocks price movements that are premature. The objective is to find stocks that are trending significantly in a positive or negative slope. Figure 1 shows stock trending higher than 20%:
Figure 1. Short Trade Potential Trends (Scott, 2016)
The stock price is increasing significantly over a period of 4 months; from December to March. (Scott, 2016) differentiated between short and long term market trading in that short term trades occur in 14 days or less. Here, the Stochastic oscillator is adjusted to 5, rather than the standard 14 for the slow period; and the standard 3 for the fast period.
The Stochastic indicator 80 and 20 levels are used to identify overbought and oversold market conditions. When the indicator is above 80, the market condition is overbought; while an indicator level below 20 indicates that the market condition is oversold. Figure 2 shows the market trend and Stochastic level from a Nasdaq composite:
Figure 2. Overbought Market Condition (Scott, 2016)
In figure 2, the Stochastic level is above 80 and the retracement and the pullbacks are coincidental in each case. This method is a fundamental visual analysis that can be used to create short term trading strategies. Please note that although this is the general interpretation of the stochastic indicator is should not be used as a stand alone indicator, it could be used as an element of a much bigger strategy.
As with gambling, in order to mitigate the risks of short term trading, appropriate risk controls that limit the size of the trades must be used. The risk aversion, or risk tolerance, a measure of how much risk the trader will comfortably assume. Global risk management frameworks have been established to prevent institutional short term trades from exposure to market losses so large that they jeopardize the viability of the institution. However, individuals who trade short term are largely responsible for the establishment of stops that will prevent financial devastation.
The cautious short term trader’s willingness to trade in response to informative signals is directly correlated with the degree that the information is reflected in the price at liquidation. The 2% Rule sets a 2% maximum loss per trade limit. The 6% Rule establishes 6% monthly drawdown cutoffs that have the capacity to break losing streaks. Further, an iron triangle of control is often used as a stop for short term investing in which point A sets the maximum risk for an account, point B sets the limit of risk per share, and point C sets the maximum number of shares.
Studies of trading behaviors provide platforms for economic and financial accounting theory that is used as public information for high-level decision making. Several models have been developed in order to systematically interpret market conditions, price movement, and investor behaviors. (Saglam, Moallemi & Sotiropoulos, 2016) supported that the disregard of the heterogeneity of short term trading expands the potential for the quantification of execution quality in short term predictive models. (Diether, Lee & Werner, 2009) presented that traders who purchase stocks that are characterized by low short-selling activity and sell short stocks that reflect high short-selling activity generate abnormal returns of approximately 1.39% (1.41%) monthly for New York Stock Exchange (NYSE) stocks.
Stable equilibria in the market may be assessed according to informational efficiency, volatility, and liquidity. (Yan & Zhang, 2009) showed that the positive correlation between future stock returns and institutional ownership is driven short term institutions; and that the trades made by short term institutions forecast the future stock returns. The method distinguished between short and long term traders based upon the trading portfolio turnover for the prior four years. In turn, (Yan & Zhang, 2009) also noted significant differences in the stock characteristics that were preferred by the short and long term traders.
(Kaniel, Saar & Titman, 2008) investigated the dynamic short horizon relationship between individual buyers and sellers using prior and subsequent returns from NYSE data. The methodology consisted of an analysis of the extent to which individual buyers and sellers’ intense net buying behaviors are associated with past returns and the extent to which the intense net trading impacts the predictions of future returns. (Kaniel, Saar & Titman, 2008) found that an in-depth understanding of the predictability of short horizon returns requires an understanding of the individual’s implicit liquidity provision and of the professional trader explicit liquidity provision.
(Cespa & Vives, 2014) presented a 2 period short term trading model that is characterized by asymmetric information that are based upon dynamic models of noisy rational expectations. Within the model, short term traders enter the market, load positions in risky assets, and the assets unwind in the second period. The methodology is based upon the assumption that persistent liquidity trading environments produce strategic complementarity in regard to private information use, and conditions that generate multiple, stable equilibria regarding volatility, price informativeness and liquidity. The outcomes of the study supported that when short term investors use persistent liquidity trading, private information is used to infer demand from the price of the first period of the model; and that the liquidity trader position significantly impacts the responses of short term traders that are risk averse.
Short term trading encompasses a diversity of outcomes that are founded upon price uncertainty; and therefore, short term trades are not ideal for investors with low risk tolerance. (Kaniel, Saar & Titman, 2008) supported that prior to considerations of explanation for evidence-based correlations between returns and NIT, it is critical to assess whether individual trading is correlated with the changes in risk associated with the stocks.
Conclusively, (Wyckoff, 1919) contributed that “success as a short term trader is typically from years of absolute focus; painstaking effort; and a devotion of all time and concentration to the tape…as no man can serve to masters, and the tape is absolutely a tyrant”.
Cespa, G. Vives, X. 2014. Expectations, Liquidity, and Short-Term Trading. Working Paper. Retrieved from http://www.hec.unil.ch/documents/seminars/ibf/1276.pdf
Diether, K. Lee, K. Werner, I. Short sale strategies and return predictability. Review of Financial Studies, 22(2), 575-607, 2009.
Kaniel, R. Saar, G. Titman, S. Individual Investor Trading and Stock Returns. The Journal of Finance, LXII(1), 2008.
Saglam, M. Moallemi, C. Sotiropoulos, M. Short-Term Trading Skill: An Analysis of Investor Heterogeneity and Execution Quality. 2016.
Scott, R. 2016. Short Term Stock Trading Strategies. Market Geeks. Retrieved from http://www.marketgeeks.com/short-term-stock-trading-strategies/
Yan, X. Zhang, Z. Institutional investors and equity returns: Are short-term institutions better informed? Review of Financial Studies, 22(2), 893-924, 2009.
Wyckoff, R. 1919. The Day Trader’s Bible. Ticker Publishing.