Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the ...existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.
The universal model-trained on data from all stocks-outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.
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Pairs trading is a popular dollar-neutral trading strategy. This article, using the components of the S&P 500 index, explores the performance of a pairs trading system based on various pairs ...selection methods. Whereas large empirical applications in the literature focus on the distance method, this article also deals with well-known statistical and econometric techniques such as stationarity and cointegration which make the trading system much more demanding from a computational point of view. Trades are initiated when stocks deviate from their equilibrium. Our results confirm, after controlling for risk and transaction costs, that the distance method generates insignificant excess returns. While a pairs selection following the stationarity criterion leads to a weak performance, this article reveals that cointegration provides a high, stable and robust return.
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493.
Noise trading and market stability Gao, Xing; Ladley, Daniel
The European journal of finance,
10/2022, Volume:
28, Issue:
13-15
Journal Article
Peer reviewed
Noise traders are often thought to be detrimental to market stability, increasing volatility and the risk of bubbles and crashes. The effect of noise traders on the learning and development of ...informed traders, however, has received little attention. We consider a computational model of a derivatives market containing informed traders and noise traders with the former group having to learn to price the traded asset. We demonstrate that noise traders have a beneficial effect on market stability: an increase in the amount of noise traders makes the market more resilient to shocks. Noise traders by pushing the price away from fundamentals create opportunities for learning, increasing the proportion of informed traders possessing high levels of trading skills in turn protecting the market.
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•Customers’ unionization has a negative impact on their supplier's stock return.•Suppliers rely less on the unionized customers for sales.•Suppliers with unionized customers diversify their customer ...base.
This study examines whether suppliers modify trading strategies upon their customers’ unionization. The study demonstrates that when customers unionize, suppliers experience negative stock returns and rely less on the unionized customers for sales. Results are robust for alternatively using a regression discontinuity design. Suppliers reduce their exposure to unionized customers due to the demand uncertainty arising from potential labor disruptions, the customers’ reduced competitiveness in the product market, and customers’ potential shifting of unionization costs to suppliers. Furthermore, suppliers with unionized customers mitigate risks by seeking new customers and diversifying their customer concentration.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a ...fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.
Why is PIN priced? Duarte, Jefferson; Young, Lance
Journal of financial economics,
02/2009, Volume:
91, Issue:
2
Journal Article
Peer reviewed
Recent empirical work suggests that a proxy for the probability of informed trading (
PIN) is an important determinant of the cross-section of average returns. This paper examines whether
PIN is ...priced because of information asymmetry or because of other liquidity effects that are unrelated to information asymmetry. Our starting point is a model that decomposes
PIN into two components, one related to asymmetric information and one related to illiquidity. In a two-pass Fama-MacBeth 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607–636 regression, we show that the
PIN component related to asymmetric information is not priced, while the
PIN component related to illiquidity is priced. We conclude, therefore, that liquidity effects unrelated to information asymmetry explain the relation between
PIN and the cross-section of expected returns.
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•The first systematic literature review on evolutionary rule discovery in stock algorithmic trading.•A clear demonstrate of studies in this field based on a classification framework.•A precise ...analysis of gaps and limitations in existing studies based on detail of evaluation scheme.•The most important factors influencing profitability of models are presented in detail.•Targeted suggestions for future improvements based on the review are proposed.
Despite the wide application of evolutionary computation (EC) techniques to rule discovery in stock algorithmic trading (AT), a comprehensive literature review on this topic is unavailable. Therefore, this paper aims to provide the first systematic literature review on the state-of-the-art application of EC techniques for rule discovery in stock AT. Out of 650 articles published before 2013 (inclusive), 51 relevant articles from 24 journals were confirmed. These papers were reviewed and grouped into three analytical method categories (fundamental analysis, technical analysis, and blending analysis) and three EC technique categories (evolutionary algorithm, swarm intelligence, and hybrid EC techniques). A significant bias toward the applications of genetic algorithm-based (GA) and genetic programming-based (GP) techniques in technical trading rule discovery is observed. Other EC techniques and fundamental analysis lack sufficient study. Furthermore, we summarize the information on the evaluation scheme of selected papers and particularly analyze the researches which compare their models with buy and hold strategy (B&H). We observe an interesting phenomenon where most of the existing techniques perform effectively in the downtrend and poorly in the uptrend, and considering the distribution of research in the classification framework, we suggest that this phenomenon can be attributed to the inclination of factor selections and problem in transaction cost selections. We also observe the significant influence of the transaction cost change on the margins of excess return. Other influenced factors are also presented in detail. The absence of ways for market trend prediction and the selection of transaction cost are two major limitations of the studies reviewed. In addition, the combination of trading rule discovery techniques and portfolio selection is a major research gap. Our review reveals the research focus and gaps in applying EC techniques for rule discovery in stock AT and suggests a roadmap for future research.
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The Internet of Things (IoT) technology is becoming increasingly pivotal in the financial services sector, with a growing number of algorithms being employed in high-frequency trading. High-frequency ...prediction in financial time series prediction presents a promising avenue of research. From convolutional neural networks to recurrent neural networks, deep learning have demonstrated exceptional capabilities in capturing the nonlinear characteristics of stock markets, thereby achieving high performance in stock index prediction. In this article, we employ ODE-LSTM model for high-frequency price forecasting, predicting stock price data across various time scales, including 1-, 5-, and 30-min frequencies. This approach introduces a novel concept, wherein the long short-term memory (LSTM) model is integrated with Neural ordinary differential equations (ODEs) to manage the hidden state and augment model interpretability. Over the course of 7 months, we achieved a 41.79% excess return on a simulated trading platform, with a daily average excess return of 0.30%, showcasing the commendable performance of our model and strategy.
The carbon pricing is the main issue of the carbon trading market for enabling cost-effective decarbonization in the energy networks. A nodal carbon pricing model is firstly proposed based on the ...sharing and integration of the intra-regional carbon emission allowance. In this regard, the game theory is introduced to construct a multi-agent carbon emission allowance bargaining model in this letter. The alternating direction multiplier method is adopted to solve the model considering the competitional burden and privacy-preserving. Numerical results demonstrate that it could significantly reduce the carbon emissions of regional energy networks and improve the economic benefits of prosumers.
I demonstrate an important tension between acquiring information and incorporating it into asset prices. As a salient case, I analyze algorithmic trading (AT), which is typically associated with ...improved price efficiency. Using a new measure of the information content of prices and a comprehensive panel of 54,879 stock-quarters of Securities and Exchange Commission (SEC) market data, I establish instead that the amount of information in prices decreases by 9% to 13% per standard deviation of AT activity and up to a month before scheduled disclosures. AT thus may reduce price informativeness despite its importance for translating available information into prices.
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