Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets

This paper explores a possible link between an asymmetric dynamic process of stock returns and profitable technical trading rules. Using the G-7 stock market indexes, we show that the dynamic process of daily index returns is better characterized by nonlinearity arising from an asymmetric reverting property. The asymmetric reverting property of stock returns is exploitable in generating profitable buy and sells signals for technical trading strategies. The bootstrap analysis shows that not all nonlinearities generate profitable buy and sell signals, but rather only the nonlinearities generating a consistent asymmetrical pattern of return dynamics can be exploitable for the profitability of the trading rules. The significant positive (negative) returns from buy (sell) signals are a consequence of trading rules that exploit the asymmetric nonlinear dynamics of the stock returns that revolve around positive (negative) unconditional mean returns under prior positive (negative) return patterns. Our results corroborate the arguments for the usefulness of technical trading strategies in stock market investments.     

Asymmetric return dynamics and technical trading strategies

We investigate the profitability of technical trading strategies based on an asymmetric reverting property of stock returns. We identify an asymmetry in return dynamics for daily returns on the S&P 500 index. Return dynamics evolve along a positive (negative) unconditional mean after a prior positive (negative) return. The trading strategies based on this asymmetry generate a positive return for buy signals, a negative return for sell signals, and a positive return for the spread between buy and sell signals. Our results imply that the observed asymmetry in return dynamics is the main source of profitability for the implied strategies, thereby corroborating arguments for the usefulness of technical trading strategies.     

Profitability of Nonlinear Dynamics Under Technical Trading Rules: Evidence from Pacific Basin Stock Markets

This paper explores a possible link between an asymmetric dynamic process of stock returns and profitable technical trading rules. Using Pacific Basin stock market indexes, we show that the dynamic process of daily index returns is better characterized by nonlinearity arising from an asymmetric reverting property, and that the asymmetric reverting property of stock returns is exploitable in generating profitable buy and sell signals for technical trading rules. We show that the positive (negative) returns from buy (sell) signals are a consequence of trading rules that exploit the asymmetric dynamics of stock returns that revolve around positive (negative) unconditional mean returns under prior positive (negative) return patterns. Our results corroborate the arguments for the usefulness of technical analysis.     

Higher Moments and Exchange Rate Behavior

This paper uses 15‐minute exchange rate returns data for the six most liquid currencies (i.e., the Australian dollar, British pound, Canadian dollar, Euro, Japanese yen, and Swiss franc) vis‐à‐vis the United States dollar to examine whether a GARCH model augmented with higher moments (HM‐GARCH) performs better than a traditional GARCH (TG) model. Two findings are unraveled. First, the inclusion of odd/even moments in modeling the return/variance improves the statistical performance of the HM‐GARCH model. Second, trading strategies that extract buy and sell trading signals based on exchange rate forecasts from HM‐GARCH models are more profitable than those that depend on TG models.     

Are investors consistent in their trading strategies? An examination of individual investor-level data

While it has been demonstrated that momentum or contrarian trading strategies can be profitable in a range of institutional settings, less evidence is available concerning the actual trading strategies investors adopt. Standard definitions of momentum or contrarian trading strategies imply that a given investor applies the same strategy to both their buy and sell trades, which need not be the case. Using investor-level, transaction-based data from China, where tax effects are neutral, we examine investors' buy-sell decisions separately to investigate how past returns impact differentially on the trading strategies investors adopt when buying and selling stock. After controlling for a wide range of stock characteristics, extreme price changes and portfolio value, a clear asymmetry in trading is observed; with investors displaying momentum behavior when buying stocks, but contrarian behavior when selling stocks. This asymmetry in behavior is not driven purely by reactions to stock characteristics or extreme stocks. We discuss behavioral and cultural explanations for our findings.     

Individual Investor Trading and Stock Returns

This paper investigates the dynamic relation between net individual investor trading and short‐horizon returns for a large cross‐section of NYSE stocks. The evidence indicates that individuals tend to buy stocks following declines in the previous month and sell following price increases. We document positive excess returns in the month following intense buying by individuals and negative excess returns after individuals sell, which we show is distinct from the previously shown past return or volume effects. The patterns we document are consistent with the notion that risk‐averse individuals provide liquidity to meet institutional demand for immediacy.     

Positive Feedback Investment Strategies and Destabilizing Rational Speculation

Analyses of rational speculation usually presume that it dampens fluctuations caused by “noise” traders. This is not necessarily the case if noise traders follow positive-feedback strategies—buy when prices rise and sell when prices fall. It may pay to jump on the bandwagon and purchase ahead of noise demand. If rational speculators' early buying triggers positive-feedback trading, then an increase in the number of forward-looking speculators can increase volatility about fundamentals. This model is consistent with a number of empirical observations about the correlation of asset returns, the overreaction of prices to news, price bubbles, and expectations.     

The composition of CMBS risk

This paper identifies the put-option, liquidity availability proportion, and shadow liquidity risk premia embedded within commercial mortgage backed securities (CMBS) using reduced form and structural generalization models. These risk values are then interpreted as trading signals which are tested with automated trading strategies that buy undervalued and sell overvalued CMBS from November 2007 through June 2015. All three signals generate substantial positive trading profits in testing for the reduced form model but not for the structural generalization. The risk signals constructed independently of market pricing provide more profitable automated trading insights than those constructed from interactions between modeled risk measures and market spreads. In my tests of the information content of the risk signals with respect to future macroeconomic indicators, I find statistically significant evidence in keeping with recent studies. While I cannot reject CMBS efficiency, this paper’s disclosure of new risk measures, the profitability of automated strategies based on those risk measures, and the statistical significance of their forward guidance capabilities, together contributes to our understanding of CMBS risk and the credit spread puzzle debate.     

Retail Investor Sentiment and Return Comovements

Using a database of more than 1.85 million retail investor transactions over 1991–1996, we show that these trades are systematically correlated—that is, individuals buy (or sell) stocks in concert. Moreover, consistent with noise trader models, we find that systematic retail trading explains return comovements for stocks with high retail concentration (i.e., small‐cap, value, lower institutional ownership, and lower‐priced stocks), especially if these stocks are also costly to arbitrage. Macroeconomic news and analyst earnings forecast revisions do not explain these results. Collectively, our findings support a role for investor sentiment in the formation of returns.     

A note on fair value pricing of mutual funds

Mutual funds claim that they employ fair value pricing to prevent active investors from trading on their beliefs that the funds’ net asset values are stale. Our results support the funds’ assertions. We estimate the returns from the following active strategy: buy international open end mutual funds that do not employ fair value pricing on days that the S&P500 index rises by a large amount, and/or sell them on days that the S&P500 index declines by a large amount. These active strategies significantly outperform pure buy-and-hold strategies. We conclude that international mutual funds should make greater use of fair value pricing.     

Business cycle variation in the risk-return trade-off

In the United States and other Organisation for Economic Co-operation and Development (OECD) countries, the expected returns on stocks, adjusted for volatility, are much higher in recessions than in expansions. We consider feasible trading strategies that buy or sell shortly after business cycle turning points that are identifiable in real time and involve holding periods of up to 1 year. The observed business cycle changes in expected returns are not spuriously driven by changes in expected near-term dividend growth. Our findings imply that value-maximizing managers face much higher risk-adjusted costs of capital in their investment decisions during recessions than expansions.     

Are chartists artists? The determinants and profitability of recommendations based on technical analysis

The value of technical analysis (TA) has been debated for decades; however, limited evidence exists on the profitability of investment recommendations issued by technical analysts. These ‘chartists’ sometimes claim that TA is an art rather than a science. We evaluated > 5000 TA-based buy and sell recommendations for stocks and a market index in the Netherlands issued during the period 2004–2010. The sign of a recommendation was generally in line with trading signals resulting from technical trading rules. While recommendation levels were positively associated with price trends prior to the recommendation, we did not find evidence of (abnormal) stock returns after the publication of these recommendations. In addition, stop-loss levels did not contain informational value as no meaningful returns were detected after these trigger levels were met. Given that technical recommendations follow well-known trading rules and that these recommendations are not associated with future abnormal returns, we conclude that technical analysts do not exhibit ‘artistic’ skills.     

The predictability of security returns with simple technical trading rules

Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper uses the daily Dow Jones Industrial Average Index from 1897 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules. The nonlinear specification of returns are modelled by single layer feedforward networks. The results indicate strong evidence of nonlinear predictability in the stock market returns by using the past buy and sell signals of the moving average rules.     

Do Industries Explain Momentum?

This paper documents a strong and prevalent momentum effect in industry components of stock returns which accounts for much of the individual stock momentum anomaly. Specifically, momentum investment strategies, which buy past winning stocks and sell past losing stocks, are significantly less profitable once we control for industry momentum. By contrast, industry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences.     

The trading behavior of institutions and individuals in Chinese equity markets

This paper employs a unique data set to analyze the trading behavior of 4.74 million individual and institutional investors across Mainland China. Results show that groups of individual investors with varying trade values (as proxies for wealth levels) engage in different trading strategies. Chinese institutions are momentum investors, while less wealthy Chinese individual investors at large are contrarian investors. The results also indicate that a small group of wealthiest Chinese individuals tend to behave like institutions when they buy stocks, and behave like less wealthy individuals when they sell. Furthermore, only the trading activities of institutions and of wealthiest individuals can affect future stock volatility, but those of Chinese individual investors at large have no predictive power for future stock returns.     

Strategic trading in the wrong direction by a large institutional insider

Many theoretical papers suggest that large informed traders should make misleading or random trades to disguise their trading. Alternatively, informed traders may trade purely on their estimate of stock value. This paper examines the trading behavior of a large institutional insider that periodically trades in the wrong direction, i.e., makes occasional sell (buy) trades within packages of buy (sell) trades. Using a hand-collected data set, we find that three quarters of the trade packages include wrong-direction trades. Wrong trades appear to be used mostly to disguise right-direction trades. We find that the wrong-trade stocks are larger and have less noisy returns, hence, they lack natural disguise. Wrong trades are relatively small, used to accentuate return volatility, distributed evenly during a package of trades, and are not consistently profitable.     

Individual and institutional herding and the impact on stock returns: Evidence from Taiwan stock market

Using high frequency intraday data, this paper investigates the herding behavior of institutional and individual investors in the Taiwan stock market. The study finds evidence of herding by both investors but a stronger herding tendency among institutional than among individual investors. Institutional investors herd more on firms with small capitalizations and lower turnovers and they follow positive feedback strategies. The portfolios that institutional investors herd buy outperform those they sell by an average of 1.009% during the 20 days after intense trading episodes. By contrast, individual investors herd more on firms with small sizes and higher turnovers, and they crowd to buy (sell) stocks with negative (positive) past returns. The portfolios that individual investors herd buy underperform those they sell by an average of − 0.829% during the following 20 days. Moreover, these return differences of both investors are more pronounced under a market with higher pressure and among small stocks. These findings suggest that the herding of institutional investors speeds up the price-adjustment process and is more likely to be driven by correlated private information, while individual herding is most likely to be driven by behavior and emotions.     

Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500

This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the algorithm efficiently determines the optimal non-linear mapping and the corresponding lead–lag structure between two time series. Afterwards, this study explores the use of optimal causal paths as a means for identifying promising stock pairs and for generating buy and sell signals. For this purpose, the established trading strategy exploits information about the leading stock to predict future returns of the following stock. The value-add of the proposed framework is assessed by benchmarking it with variants relying on classic similarity measures and a buy-and-hold investment in the S&P 500 index. In the empirical back-testing study, the trading algorithm generates statistically and economically significant returns of 54.98% p.a. and an annualized Sharpe ratio of 3.57 after transaction costs. Returns are well superior to the benchmark approaches and do not load on any common sources of systematic risk. The strategy outperforms in the context of cryptocurrencies even in recent times due to the fact that stock returns contain substantial information about the future bitcoin returns.     

Institutional trading and stock returns

In this study, we explore the dynamics of the relation between institutional trading and stock returns. We find that stock returns Granger-cause institutional trading (especially purchases) on a quarterly basis. The robust and significant causality from equity returns to institutional trading can be largely explained by the time-series variation of market returns, that is, institutions buy more popular stocks after market rises. Stock returns appear to be negatively related to lagged institutional trading. A further analysis of the behavior of trading and the returns of the traded stocks reveals evidence that stocks with heavy institutional buying (selling) experience positive (negative) excess returns over the previous 12 months.     

The interaction between foreigners' trading and stock market returns in emerging Europe

This paper presents the first comprehensive study of foreigners' trading in European emerging stock markets, using complete data compiled at the destination. We also compare European results to Asia, to which available evidence is mainly confined. The findings provide new insight on foreigners' trading: in addition to rebalancing, risk appetite and global information are global drivers of net foreign flows. Foreigners' negative-feedback-trade with respect to local returns at longer horizons. They do so with an asymmetry: they sell following increases but do not buy following declines. Net foreign flow persistence decreases with local return volatility.