Predicting Stock Price Changes Based on the Limit Order Book: A Survey

An experimental approach to the study of limit order books lies in the availability of data. Most of the results presented in this book - in any case, those we have produced ourselves - use the Thomson Reuters Tick History (TRTH) database. All exchange-traded assets worldwide are present in the TRTH database, where they are identified by their Reuters Identification Code (RIC). Similar to most historical databases directly provided by the exchanges, the TRTH data come into the form of two separate files, a trade file recording all transactions, and an event file recording every change in the limit order book. Some very specific information, such as traders’ identities, cannot be publicly disclosed for obvious confidentiality reasons, but in theory, one could reconstruct the sequence of order arrivals of all types using this trade and event files. After explaining the algorithm used for the processing of limit order book data, we describe the specific data sets that have been used at various places in this book. That way, our results can be reproduced, extended and, of course, challenged, based on the very same data sets we have used. Limit Order Book Data Processing Because one cannot distinguish market orders from cancellations just by observing changes in the limit order book (the “event” file), and since, the timestamps of the “trade” and “event” files are asynchronous , we use a matching procedure to reconstruct the order book events. In a nutshell, we proceed as follows for each stock and each trading day: 1. Parse the “event” file to compute order book state variations: • If the variation is positive (volume at one or more price levels has increased), then label the event as a limit order. • If the variation is negative (volume at one or more price levels has decreased), then label the event as a “likely market order”. • If no variation—this happens when there is just a renumbering in the field “Level” that does not affect the state of the book—do not count an event. 2. Parse the “trade” file and for each trade: • Compare the trade price and volume to likely market orders whose timestamps are in, where t Tr is the trade timestamp and Δ t is a predefined, market-dependent time window.

We introduce new machine learning methods for clustering traders who are actively trading in a modern electronic exchange which uses a matching engine to track aggregate and individual‐level limit order books. Each trader's individual limit order book is centered (with the current best bid and ask prices acting as a central reference), and the patterns in the individual limit order books are identified using a Wasserstein distance. The method is illustrated using simulated limit order book data and limit order book data from a stock exchange in Canada.

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation, and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.

The limit order book of an exchange represents an information store of market participants’ future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world’s most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges.

Internalization in European Equity Markets Following the Adoption of the EU Mifid Directive

The paper considers a general semi-Markov model for limit order books with two states that incorporates price changes that are not fixed to one tick. Furthermore, we introduce an even more general case of the semi-Markov model for limit order books that incorporates an arbitrary number of states for the price changes. For both cases, the justifications, diffusion limits, implementations and numerical results are presented for different limit order book data: Apple, Amazon, Google, Microsoft, Intel on 21 June 2012 and Cisco, Facebook, Intel, Liberty Global, Liberty Interactive, Microsoft, Vodafone from 3 November 2014 to 7 November 2014.

<abstract><p>In this paper, we examine the usefulness of machine learning methods such as support vector machines, random forests and bagging for the extraction of information from the limit order book that can be used for intraday trading. For our empirical analysis, we first get 50 raw features from the LOBSTER message file and order book file of the iShares Core S & P 500 ETF for the time period from 27.06.2007 to 30.04.2019 and then construct 18 higher-level features (aggregated to 5 minutes frequency) which serve as predictors. Using straightforward specifications for the machine learning procedures and thereby avoiding excessive data snooping, we find that these procedures are unable to find high dimensional patterns in the order book that could be used for trading purposes. The observed significant predictability is mainly due to the inclusion of only one variable, namely the last price change, and is probably too small to ensure profitability once transaction costs are taken into account.</p></abstract>

From past to present, the prediction of stock price in stock market has been a knotty problem. Many researchers have made various attempts and studies to predict stock prices. The prediction of stock price in stock market has been of concern to researchers in many disciplines, including economics, mathematics, physics, and computer science. This study intends to learn fluctuation of stock prices in stock market by using recently spotlighted techniques of deep learning to predict future stock price. In previous studies, we have used price-based input-features to measure performance changes in deep learning models. Results of this studies have revealed that the performance of stock price models would change according to varied input-features configured based on stock price. Therefore, we have concluded that more novel input-feature in deep learning model is needed to predict patterns of stock price fluctuation more precisely. In this paper, for predicting stock price fluctuation, we design deep learning model using 715 novel input-features configured on the basis of technical analyses. The performance of the prediction model was then compared to another model that employed simple price-based input-features. Also, rather than taking randomly collected set of stocks, stocks of a similar pattern of price fluctuation were filtered to identify the influence of filtering technique on the deep learning model. Finally, we compared and analyzed the performances of several models using different configuration of input-features and target-vectors.

Multilinear class-specific discriminant analysis

The stock market price movement has become an interesting topic to research due to the large profit potential. Market traders use technical analysis to forecast stock prices by analyzing stock transaction data. Because technical analysis predictions are subjective, an automated system is needed to produce more objective and faster results. In the previous, next prices were predicted by combining price and transaction volume features with technical analysis indicators such as Stochastic, Moving Average, Moving Average Convergence Divergence, and Relative Strength Index values. In most cases, the addition of the indicator's value reduces the accuracy of the forecast. This is because the value of technical analysis indicators is not useful enough when they are used directly without transformation. The transformation of technical analysis indicators to a crossing value between the fast and slow signal on these indicators, known as the golden cross and death cross, has a greater impact on stock price movement. Therefore, it is recommended to use the price and the golden cross and death cross on technical analysis indicators as inputs to improve stock price predictions. Long Short Term Memory is used in the proposed method, which is a reliable method for predicting sequence data. The stock price forecasts are based on 1200 days of trading data from the Indonesia Stock Exchange for 10 LQ45 group shares. The test was conducted by calculating the Root Mean Squared Error and the Mean Absolute Percentage Error value of the predicted close price next day.

Stock price trend prediction has been a hot issue in the financial field, which has been paid much attention by both academia and industry. It is a challenging task due to the non-stationary and high volatility of the stock prices. Traditional methods for predicting stock price trends are mostly based on the historical OHLC (i.e., open, high, low, and close prices) data. However, it eliminates most of the trading information. To address this problem, in this paper, another type of stock price data, i.e., limit order books (LOBs), is used. For better exploring the relationship of the LOBs and stock price trend, inspired by the successful application of deep learning-based methods, an attention-based LSTM model is applied. The trend of stock price can be predicted by using the LOBs data of the previous day. By using the real stock price data of the China stock market, the effectiveness of the proposed model is validated by experimental results.

This study aims to identify one of the indicators of technical analysis, which is the simple moving average (SMA), and its impact on the movement of stock prices in private Iraqi commercial banks, by standing on the methods used in technical analysis and its various Models in general, and ways to benefit from them in determining the direction of stock prices in order to make the appropriate investment decision to buy or sell, diagnose the obstacles and difficulties that the participants face in predicting stock prices, and propose solutions and recommendations to overcome and overcome them from a scientific and practical perspective. Where the research community consisted of (25) Iraqi private commercial banks, while the research sample consisted of (5) banks with a rate of (20%) of the research community. The study used the analytical approach to the financial statements during the period between (1/2/ 2022-30/4/2022, where the financial data (banks stock prices) were calculated through the mathematical equation of the Simple Moving Average Index (SMA)) .

In the world of capital markets, indicators are used to measure changes in stock prices. A stock index is an indicator to see information about stock price movements. The existence of various information both from within the country and abroad is one of the external factors that causes the movements of stock prices to change significantly. This study aims to analyze the price movements of the companies' shares included in the LQ45 Index from the impact of a big event that is currently happening, namely the pandemic Covid-19. This study uses the method of Survival Analysis with the approach Kaplan Meier, variable of this study uses stock prices. The results of the research that have been conducted found that external factors, namely information about Covid-19 that spread throughout the world, given a negative impact on stock price movements on the LQ45 Index, namely 45 LQ45 companies experienced a decline in stock prices during the observation period, 5 of 45 companies still experienced a declined. After the observation period took place, viewed by sector, eight sectors included in LQ45 experienced a decline in stock prices at the same time during the period 02 January to 31 March 2020.

Although the prediction of stock prices is a topic that has been studied in several occasions, there are few variables that have been used for this matter outside of dividends such as determinant of prices (Guo, 2022). The results found to date have not been entirely satisfactory. since they do not explain most price movements in the long run. The use of profits such as predictor variable is an alternative that has not been much explored. This research work seeks to answer what is the effect of using profits as a predictor of stock prices in the long run. With the In order to answer this question, a panel regression was performed, controlling for fixed effects, of the effect of the profits on stock prices over the past 30 years for S&P 500 companies. In general, it was not possible to determine if the profits, whether gross, operating or net, have the capacity to predict changes in stock prices. However, it was established that the size of the assets of a company and changes in US GDP have a negative relationship with changes in price. By On the other hand, the change in the price of the SPX index has a positive relationship with the change in the stock price. This finding can generate a great advantage for investors when creating stock portfolios in the long term that seek to maximize profitability with a certain degree of certainty, since it would allow choosing with greater clarity those actions with greater probability of success and returns in the long

Price discovery, order submission, and tick size during preopen period