Profitability of technical trading strategies under market manipulation

Most technical trading strategies use the official closing price for analysis. But what is the effect when the official closing price is subject to market manipulation? This paper answers this question by testing the difference of profitabilities between using the official closing price and the last tick price. The results show a significant improvement of profitability by using the last tick price over the official closing price based on a data set in Hong Kong from 2011 to 2018.

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q-Network (I-PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I-PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision-making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

Institutional Herding: Causality and Persistence

We investigate the causality in herding between foreign portfolio investors (FPIs) and domestic mutual funds (MFs) in the Indian stock market. The estimated herding levels are considerably higher than those observed in other international markets, and herding is prevalent in small stocks. We find that institutional investors follow contrarian-trading strategies, unlike what was documented in most other markets. Analysis of the aggregate herding measure shows a bi-directional causality between FPIs and MFs. Further analysis using directional herding measures indicate no evidence of causality between institutional herds on the sell-side. But we find causality on the buy-side and it is running in both directions between FPIs and MFs, implying a feedback of information. Given the tendency of institutions for herding in small stocks, adopting contrarian-trading strategies, the observed sell-side causality is perhaps having a salubrious effect. As institutional investors are contrarians, their trading activity will lead to price corrections in small stocks aligning with the fundamentals, thereby contributing to market efficiency. JEL Classification: C23, C58, G23, G15, G40

Profitability vs. Stability: The Choice of Trading Strategies in a Capacity Sharing Supply Chain

Supported by a third-party capacity sharing platform, manufacturers can share capacities with others to match the rapidly changing demand. Both the capacity requestor and the capacity provider can choose to seek or wait for matches, forming different trading strategies (capacity- and demand-driven strategies). Based on the game and chaos theories, this paper analyzes the preference of the capacity provider, the capacity requestor, and the capacity platform operator on different trading strategies from the aspects of profitability and stability. It finds that the platform operator values stability much more than profitability, although the latter may reach higher optimal expected profits. The preference of each supply chain member is influenced by the production cost, potential market size, and the limited capacity of the capacity requestor. A stable system can result in higher long-run profits than a profitable system. We further propose the all-win situation for the capacity provider, capacity requestor, and platform operator.

The Initial Analysis of the Share Suitability for the Inclusion into an Active Trading Strategy

The article lists the problems inherent to the Russian stock market at the present stage, special attention is paid to the liquidity issues. The authors evaluate the shares of all issuers listed on the Moscow Stock Exchange for the possibility of their inclusion in an active strategy based on average trading volumes. The article calculates the effectiveness of using the methods of average values in assessing the compliance of the selected instruments with the minimum required liquidity values. In the course of the work, the industry features of the Russian market are taken into account. The classifier of the Moscow Exchange is used to distribute issuers by industry. In parallel, the liquidity imbalance between the branches of the Russian stock market is being investigated. The conclusion is given about the real number of stock market instruments suitable for use in active trading strategies. The result of the study is a formed set of shares distributed by industry.

A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks.

Beating the market? A mathematical puzzle for market efficiency

The efficient market hypothesis is highly discussed in economic literature. In its strongest form, it states that there are no price trends. When weakening the non-trending assumption to arbitrary short, small, and fully unknown trends, we mathematically prove for a specific class of control-based trading strategies positive expected gains. These strategies are model free, i.e., a trader neither has to think about predictable patterns nor has to estimate market parameters such as the trend’s sign like momentum traders have to do. That means, since the trader does not have to know any trend, even trends too small to find are enough to beat the market. Adjustments for risk and comparisons with buy-and-hold strategies do not satisfactorily solve the problem. In detail, we generalize results from the literature on control-based trading strategies to market settings without specific model assumptions, but with time-varying parameters in discrete and continuous time. We give closed-form formulae for the expected gain as well as the gain’s variance and generalize control-based trading rules to a setting where older information counts less. In addition, we perform an exemplary backtesting study taking transaction costs and bid-ask spreads into account and still observe—on average—positive gains.