Superior Trading Research Articles

Is the Korea Fair Trade Commission Abusing the Provision for Abuse of Superior Trading Position?: A Critical Analysis of the Act on Fair Intermediary Trade of Online Platforms

Abstract This paper examines the economic impact of the abuse of superior trading position (ASTP) provision within Korea’s draft Act on Fair Intermediary Trade of Online Platforms (AFITOP). The AFITOP, proposed as part of competition law reform in the digital era, applies to large online platform operators providing intermediary services. While derived from the Monopoly Regulation and Fair Trade Act (MRFTA), the ASTP provision’s goal to protect the economically weak is secondary to the MRFTA’s primary goal of efficiency. Furthermore, the provision’s interpretative uncertainties burden enterprises, especially large ones and those exploring new business models. Including this provision in the AFITOP is likely to reduce innovation incentives among large online platform operators, and to foster enforcement bias toward the ASTP provision by the Korea Fair Trade Commission, thereby increasing inefficiency in competition law enforcement. Therefore, the ASTP provision in the AFITOP should not be introduced.

On Data-Driven Drawdown Control with Restart Mechanism in Trading

This paper extends the existing drawdown modulation control policy to include a novel restart mechanism for trading. It is known that the drawdown modulation policy guarantees the maximum percentage drawdown no larger than a prespecified drawdown limit for all time with probability one. However, when the prespecified limit is approaching in practice, such a modulation policy becomes a stop-loss order, which may miss the profitable follow-up opportunities, if any. Motivated by this, we add a data-driven restart mechanism into the drawdown modulation trading system to auto-tune the performance. We find that with the restart mechanism, our policy may achieve a superior trading performance to that without the restart, even with a nonzero transaction costs setting. To support our findings, some empirical studies using equity ETF and cryptocurrency with historical price data are provided.

On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach

In this paper, we propose an online data-driven sliding window approach to solve a log-optimal portfolio problem. In contrast to many of the existing papers, this approach leads to a trading strategy with time-varying portfolio weights rather than fixed constant weights. We show, by conducting various empirical studies, that the approach with proper choice of window size possesses a superior trading performance to the classical log-optimal portfolio and mean-variance portfolio in the sense of having a higher cumulative rate of returns.

Can Individual Investors Beat the Market?

Abstract We document persistent superior trading performance among a subset of individual investors. Investors classified in the top performance decile in the first half of our sample subsequently earn risk-adjusted returns of about 6% per year. These returns are not confined to stocks in which the investors are likely to have inside information, nor are they driven by illiquid stocks. Our results suggest that skilled individual investors exploit market inefficiencies (or perhaps conditional risk premiums) to earn abnormal profits, above and beyond any profits available from well-known strategies based on size, value, momentum, or earnings announcements. (JEL G11, G14, G40, G51) Received: October 11, 2020 Editorial decision: January 4, 2021 Editor: Jeffrey Pontiff

High-Frequency Technical Analysis and Systemic Risk Indicators

This study conducts a high-frequency technical analysis of individual stocks listed on the Tokyo Stock Exchange. We propose novel technical rules that derive the timing of trades according to traditional systemic risks—such as shock-propagation, quote-stuffing, and tail risks—measured by auto- and cross-correlations in order flows, quote-to-trade ratios, and CoVaRs. We demonstrate that both price-based technical strategies—commonly used in technical analysis such as moving average rules—and the newly proposed rules can exploit the significantly superior performance to the buy-and-hold rule when we trade volatile momentum or trend-reversal stocks of small-sized firms. Accordingly, this study improves stock price forecasts in high-frequency trading. Our results suggest that historic prices and systemic risk indicators assist in the risk management and portfolio choices of stock investors. To the best of our knowledge, this is the first study to demonstrate the superior trading performance of individual stocks using a high-frequency technical analysis—even after considering data-snooping bias and transaction costs.

An intelligent quantitative trading system based on intuitionistic-GRU fuzzy neural networks

Quantitative trading based on intelligent algorithms has been a hot topic in the financial fields. However, the high noises and outliers increase the uncertainty and non-stationary of financial data such that intelligent algorithms often cease to be effective in the application scenario. Therefore, how to reduce the influence of uncertainty and extract the main trends of financial data is meaningful for trading. Motivated by it, in this article, a novel trading system based on intuitionistic fuzzy neural networks with gated recurrent unit (GRU) is proposed. Firstly, empirical mode decomposition (EMD) is applied to preprocess the original data and obtain the main trends of financial time series on the basis of smoothing data. Secondly, in order to tackle with uncertainty of financial data, a novel interval type-2 intuitionistic fuzzy system (IT2IFS) is proposed for the reasoning process, where hesitancydegree is involved for the learning processes In view of the strong dependence on the time dimension of series data, the gated recurrent unit is integrated into the reasoning model to strengthen the temporal connection. Thirdly, by using the above IT2IFS-GRU model, the quantitative trading system with a concise trading strategy is constructed. By using commodity futures and foreign exchange data, the superior trading results including net profits and precisions, etc. can be obtained, which verifies the effectiveness and availability of the proposed model.

How Smart is Instiutional Trading?

We estimate daily aggregate order flow of individual stocks from institutional investors as well as for hedge funds and other institutions separately. We find that the estimated institutional order imbalance positively predicts stock return on the next day and outperforms other institutional order flow estimates. The institutional order flow from hedge funds generates greater and more persistent price impact than the order flow from other institutions. We also find that hedge funds trade on well-known anomalies while the other institutions do not. Our findings suggest that the superior trading skills of institutional investors can be largely attributed to hedge funds.

Market timing with moving averages for fossil fuel and renewable energy stocks

The paper examines whether the Moving Average (MA) technique can outperform random market timing in the energy sector, compiled of fossil and renewable energy producers. According to the Capital Asset Pricing Model, random timing is a superior trading strategy in the long run. However, the MA technique may be more successful, if there are predictable stochastic trends in the price series. In the paper, eight representative firms are selected for both fossil and renewable portfolios with actually tradable stocks in order to create two Exchange-Traded Funds (ETF). The paper finds that MA timing outperforms random timing for the ETF of renewable energy companies, but not for the ETF of fossil energy companies.

Do business ties generate private information? Evidence from institutional trading around M&A announcements

Business ties generate private information for institutional investors related to a firm's 401(k) trustee. These investors strategically trade a bidder's stock around the announcement of a deal, reducing holdings ahead of value-destroying deals and increasing their positions before value-creating ones. Superior trading ability does not explain this behavior as these same investors do not exhibit similar trading behavior in firms with which they do not have a business tie.

THE FUNDAMENTAL THEOREMS OF ASSET PRICING AND THE CLOSED-END FUND PUZZLE

We propose a solution to the closed-end fund puzzle in financial markets without a free lunch with vanishing risk. Our results are consistent with both the time-series and the cross-sectional aspect of the closed-end fund puzzle. It turns out that a closed-end fund cannot exist if the fund manager is supposed to receive a fee although he is not able to find mispriced assets in the market. By contrast, a premium can typically be observed at the initial public offering because the fund manager has access to information that enables him to create a dominant strategy. As soon as this weak arbitrage opportunity evaporates, a premium can no longer occur. The reason why a premium quickly turns into a discount might be that the fund manager stops applying a superior trading strategy at some point in time. Another possibility is that abnormal profits are transient in a competitive financial market. In any case, when the fund manager is no longer willing or able to maintain a superior strategy, the fund must trade at a discount in order to compensate for his management fee.

Short‐Term Trading Skills of Individuals, Institutions, and Foreigners: A New Approach Based on Relative Performance

AbstractThis paper evaluates the short‐term trading skills of three types of investor: individuals, institutions, and foreigners. Based on a dataset that identifies the types of sellers and buyers for all trades, we examine (i) how stock prices move contemporaneously with trading activities between investor types, and (ii) whether, given such price movements, one type of investor has superior skills in timing purchases (sales) of stocks over another type of investor. Using tests based on daily and weekly horizons, we find evidence that individuals have poor trading skills and provide liquidity to both institutions and foreigners. When domestic institutions trade with foreigners, we find that foreigners generally show superior trading skills.

Genetic programming optimization for a sentiment feedback strength based trading strategy

This study is motivated by the empirical findings that news and social media Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strategy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy using investors’ sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feedback based strategies yield superior market returns with low maximum drawdown over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the basic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown.

Uncovering Skilled Short-Sellers

Using a deal-level data set we classify all Brazilian short-sellers according to their performance and trading behavior. Our goal is to uncover the “skilled short-sellers.” We analyze their deals in isolation to study their superior trading performance. Skilled short-sellers earn on average 1.67% more per deal than unskilled short-sellers, with 73% of this superior performance coming from stock-picking skill and the remaining 27% coming from market-timing skill. Skilled short-sellers are also proficient at choosing when to cover their positions. Unlike unskilled short-sellers, skilled short-sellers behave as short-term momentum investors and display no disposition effect.

Event-Based Trading: Building Superior Trading Strategies with State-of-the-Art Information Extraction Tools

Advanced sentiment analysis systems, which typically produce a positive or negative signal on firms for financial traders, consider financial events when calculating aggregate sentiment scores. However, these systems typically produce overly noisy signals for traders because they aggregate the information culled from the events rather than address each distinct event-type differently (all event-types receive the same weight in a text’s final sentiment score). We demonstrate that event-types have different informative values for traders, and specific event-types may be particularly relevant for specific investment periods. In this work we evaluate and compare the effects of event-type signals and sentiment analysis signals, automatically extracted from a given text, on stock prices in different investment periods. First, we establish that the polarity of different event-types is reflected in the stock price with different time lags. Second, we use benchmark portfolio and show that, when considering the polarity of different events for different time periods, one can generate excess return. Finally, we use the Quantopian Research Platform to test investment strategies based on different event-types and based on sentiment analysis signals, over various validation periods. For the experiments, we extracted event-types and sentiments from a large dataset of articles on a large number of firms using VIP, a state-of-the-art information extraction platform, and obtained the corresponding historical stock prices. We show that traders using trading strategies based on event-types and sentiment analysis obtain superior performance compared to acceptable benchmarks. We further show that when such investment strategies are based on daily aggregated event score, performance is not superior to strategies based on daily sentiment analysis scores. Performance is enhanced further when strategies use select event-type scores. Finally, we show that different event-types have different temporal effects on portfolio performance.

The Gender Face-Off: Do Females Come Out on Top in Terms of Trading Performance?

We study the relation between gender and stock trading performance using data on all trades by individual Finnish investors over 1995-2011. Following “holding-period-invariant” (HPI) portfolio approach - without imposing a heroic assumption that all individual investors rebalance their portfolio at specified intervals corresponding to an assumed horizon, we document significant and remarkable gain made by female investors at the expense of male investors in trading major Finnish stocks, yielding an impressive internal rate of return of 21.44% p.a. Our findings are consistent with females tending to have better ability in the “theory of the mind”, and hence are better at recognising patterns in the data with superior trading intuition. We further show that female investors prefer to purchases under-priced stocks and sell overpriced stocks based on the trading signal that the difference between contemporaneous price and moving averages over short-term, 4-week to long-term, 1-year. Our evidence suggests that it is possible to use the trading pattern of these smart female investors to anticipate market movements. We also identify the spouse trading accounts of insiders as we can match family names. Even when the accounts of these superior traders are removed we still find evidence that females are superior traders, especially in the greater Helsinki region close to Nokia and other company headquarters.

Trading Better Versus Making More: Evidence from an Emerging Market

ABSTRACTUsing transaction data from Egypt, we examined the controversy over which investor—domestic or foreign—has superior trading performance in emerging markets. We account for informational and behavioral differences across investors by classifying them by origin and type and comparing their performance in trade execution versus profitability. Domestic institutions execute trades at the best prices with the greatest advantage against foreign institutions. This advantage is reduced when foreign institutions focus on large firms and trades. Profitability analysis revealed, however, that domestic investors accrue significant losses against foreign investors, suggesting that trading better does not necessarily translate into making more money.

Beating the market: Can evolutionary-based portfolio optimisation outperform the Talmudic diversification strategy?

It is argued that with a small number of stocks (N) in a portfolio (which suits individuals rather than institutional investors), naive Talmudic diversification rule (1/N) offers a superior trading outcome against mathematically optimal portfolios due to its robustness against estimation error. As this puzzle has not been resolved, we explore it using an alternative portfolio choice problem that seeks to outperform the benchmark market index - FTSE Bursa Malaysia KLCI. This study makes a significant contribution by using an industry-common objective function and also incorporating floor/ceiling constraints and the effect of delisting. Using evolutionary algorithm, we construct optimal portfolios with varying Ns in-sample for out-of-sample analysis. We find that 1/N is superior with smaller Ns, although optimised portfolio dominates as N increases. However, with both diversification policies underperform the market and produce very low Sharpe ratios, their efficacies for practical applications are highly suspect.

An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy

News sentiment has been empirically observed to have impact on financial market returns. In this study, we investigate firm-specific news from the Thomson Reuters News Analytics data from 2003 to 2014 and propose an optimal trading strategy based on a sentiment shock score and a sentiment trend score which measure extreme positive and negative sentiment levels for individual stocks. The intuition behind this approach is that the impact of events that generate extreme investor sentiment changes tends to have long and lasting effects to market movement and hence provides better prediction to market returns. We document that there exists an optimal signal region for both indicators. And we also show extreme positive sentiment provides better a signal than extreme negative sentiment, which presents an asymmetric market behavior in terms of news sentiment impact. The backtest results show that extreme positive sentiment generates robust and superior trading signals in all market conditions, and its risk-adjusted returns significantly outperform the S&P 500 index over the same time period.

Economic total maximum daily load for watershed-based pollutant trading

Water quality trading (WQT) is supported by the US Environmental Protection Agency (USEPA) under the framework of its total maximum daily load (TMDL) program. An innovative approach is presented in this paper that proposes post-TMDL trade by calculating pollutant rights for each pollutant source within a watershed. Several water quality trading programs are currently operating in the USA with an objective to achieve overall pollutant reduction impacts that are equivalent or better than TMDL scenarios. These programs use trading ratios for establishing water quality equivalence among pollutant reductions. The inbuilt uncertainty in modeling the effects of pollutants in a watershed from both the point and nonpoint sources on receiving waterbodies makes WQT very difficult. A higher trading ratio carries with it increased mitigation costs, but cannot ensure the attainment of the required water quality with certainty. The selection of an applicable trading ratio, therefore, is not a simple process. The proposed approach uses an Economic TMDL optimization model that determines an economic pollutant reduction scenario that can be compared with actual TMDL allocations to calculate selling/purchasing rights for each contributing source. The methodology is presented using the established TMDLs for the bacteria (fecal coliform) impaired Muddy Creek subwatershed WAR1 in Rockingham County, Virginia, USA. Case study results show that an environmentally and economically superior trading scenario can be realized by using Economic TMDL model or any similar model that considers the cost of TMDL allocations.

Do Day Traders Rationally Learn About Their Ability?

We analyze the performance of and learning by individual investors who engage in day trading in Taiwan from 1992 to 2006 and test the proposition that individual investors rationally speculate as day traders in order to learn whether they possess the superior trading ability. Consistent with models of both rational and biased learning, we document that unprofitable day traders are more likely to quit and that day traders begin with relatively small trades that increase as they gain experience. Inconsistent with models of rational speculation and learning, we document that the aggregate performance of day traders is negative and that over half of day trading can be traced to traders with considerable experience and a history of losses.