Past Price Changes Research Articles

Stock price prediction with long short-term memory

Stock forecasting aims to predict future stock prices based on past price changes in the market, playing an essential role in the field of financial transactions. However, since the stock market is highly uncertain, stock prediction is complex and challenging. This paper uses the long short-term memory (LSTM) model to predict the stock market and compares it with the current stock prediction algorithm. Firstly, we preprocessed the raw dataset and normalized data into the range from 0 to 1. Secondly, we introduced the LSTM model and improved its performance by tuning four parameters: learning rate, number of hidden layers, number of epochs, and batch size. Finally, we use four evaluation metrics to evaluate models: mean average error (MAE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error percentage (MAPE). Our LSTM model performs better than the previous model in experiments in terms of MAE, RMSE, R2, and MAPE.

The role of the past long-run oil price changes in stock market

This paper examines the ability of the past long-run changes in oil price to predict the stock returns in the U.S. market. We find this long-lag model performs much better than the one-lag model. The past long-run changes in oil price contain useful information about future real stock returns and excess returns over a Treasury bill rate. This variable alone can capture more than 1% variations of next horizon (month) excess returns, and the predictive power are increasingly strong for long-horizon stock return. These findings are robust when considering other popular predictors into the model, these results are also maintained when considering various subsamples. For out-of-sample examination, the results of McCraken’s (2007) Ros2 and Clark and West’s (2007) MSPE-adjusted statistic explore that this variable contains useful information of future stock returns. More interestingly, the past long-run oil price changes also perform strong predictive power on excess returns for non-US countries.

Search intensity, search time and prices: evidence from retail diesel markets in France

We study the effect of price variations over time and across space on search intensity and search time by consumers in retail markets for diesel in France. The main contribution of the paper is that existing work in industrial organisation in this area has already studied the effect of such variability on the first measure of search but not on the second one. Using novel data from daily consultation measures and price information from a government-run website, we find that price dispersion across space increases search activity and the amount of time allocated to search. Furthermore, while contemporaneous price changes do not appear to influence the number of visits and time per visit to the website, several coefficients on past price changes are positive and statistically different from zero. Our results thus suggest that price dispersion and price variability play a role in inducing search by current and potential customers.

The Impact of Historical Price Information on Purchase Deferral

In this article, the authors examine how historical price information influences consumers’ decision to defer a purchase. They focus on two aspects of historical price information: the direction and the frequency of past price changes. The authors advance a theoretical framework postulating that the interaction between these two factors shapes consumers’ decisions to buy now versus later. Controlling for the total magnitude of price changes, the authors propose that consumers are more likely to defer purchase when the price of the product has previously increased compared with when the price has decreased. Importantly, the authors hypothesize that this effect is more pronounced when consumers observe a single large change in price (e.g., a decrease of $100 vs. an increase of $100) compared with when they observe multiple smaller changes that establish a trend (e.g., four decreases of $25 vs. four increases of $25). The authors argue that these effects are driven by differences in consumers’ expectations about future prices. They test their predictions, as well as two moderators of the proposed effects—the monotonicity and the timing of price changes—in six well-powered preregistered experimental studies (N = 5,713) using both hypothetical and actual purchases.

Two-Stage Hybrid Machine Learning Model for High-Frequency Intraday Bitcoin Price Prediction Based on Technical Indicators, Variational Mode Decomposition, and Support Vector Regression

Due to the inherent chaotic and fractal dynamics in the price series of Bitcoin, this paper proposes a two-stage Bitcoin price prediction model by combining the advantage of variational mode decomposition (VMD) and technical analysis. VMD eliminates the noise signals and stochastic volatility in the price data by decomposing the data into variational mode functions, while technical analysis uses statistical trends obtained from past trading activity and price changes to construct technical indicators. The support vector regression (SVR) accepts input from a hybrid of technical indicators (TI) and reconstructed variational mode functions (rVMF). The model is trained, validated, and tested in a period characterized by unprecedented economic turmoil due to the COVID-19 pandemic, allowing the evaluation of the model in the presence of the pandemic. The constructed hybrid model outperforms the single SVR model that uses only TI and rVMF as features. The ability to predict a minute intraday Bitcoin price has a huge propensity to reduce investors’ exposure to risk and provides better assurances of annualized returns.

Non-parametric estimation of quadratic Hawkes processes for order book events

We propose an actionable calibration procedure for general Quadratic Hawkes models of order book events (market orders, limit orders, cancellations). One of the main features of such models is to encode not only the influence of past events on future events but also, crucially, the influence of past price changes on such events. We show that the empirically calibrated quadratic kernel is well described by a diagonal contribution (that captures past realised volatility), plus a rank-one ‘Zumbach’ contribution (that captures the effect of past trends). We find that the Zumbach kernel is a power-law of time, as are all other feedback kernels. As in many previous studies, the rate of truly exogenous events is found to be a small fraction of the total event rate. These two features suggest that the system is close to a critical point – in the sense that slightly stronger feedback kernels would lead to endogenous liquidity crises.

주택특성 및 가구특성이 주택구입자금 부채비율에 미치는 영향

The aim of the study is to find out how the home purchasing capital structures of people vary as expected rate of return of house rise or fall in prices through past experience of price change. The result from the area where its expected price change have positive number appeared to have high debt capital ratios in house purchase financing. It was, in the area of expected price change is negative, it was difficult to find statistical significance between the two. In addition, net assets(-) and income(+), the age of head of household(-), housing size(+) showed similar results with a previous study. The study suggesting that local and regional characteristics like past price changes and expectations derived from past price changes can be considered as an additional element in the loan terms. This is because, household can bring up the loan financing demand in certain area where price expectations rise.

Endogenous liquidity crises

Empirical data reveals that the liquidity flow into the order book (limit orders, cancellations and market orders) is influenced by past price changes. In particular, we show that liquidity tends to decrease with the amplitude of past volatility and price trends. Such a feedback mechanism in turn increases the volatility, possibly leading to a liquidity crisis. Accounting for such effects within a stylized order book model, we demonstrate numerically that there exists a second order phase transition between a stable regime for weak feedback to an unstable regime for strong feedback, in which liquidity crises arise with probability one. We characterize the critical exponents, which appear to belong to a new universality class. We then propose a simpler model for spread dynamics that maps onto a linear Hawkes process which also exhibits liquidity crises. If relevant for the real markets, such a phase transition scenario requires the system to sit below, but very close to the instability threshold (self-organised criticality), or else that the feedback intensity is itself time dependent and occasionally visits the unstable region. An alternative scenario is provided by a class of non-linear Hawkes process that show occasional ‘activated’ liquidity crises, without having to be poised at the edge of instability.

Non-parametric Estimation of Quadratic Hawkes Processes for Order Book Events

We propose an actionable calibration procedure for general Quadratic Hawkes models of order book events (market orders, limit orders, cancellations). One of the main features of such models is to encode not only the influence of past events on future events but also, crucially, the influence of past price changes on such events. We show that the empirically calibrated quadratic kernel is well described by a diagonal contribution (that captures past realised volatility), plus a rank-one “Zumbach” contribution (that captures the effect of past trends). We find that the Zumbach kernel is a power-law of time, as are all other feedback kernels. As in many previous studies, the rate of truly exogenous events is found to be a small fraction of the total event rate. These two features suggest that the system is close to a critical point – in the sense that stronger feedback kernels would lead to instabilities.

Economic Consequences of Housing Speculation

AbstractBy exploiting variation in state capital gains taxation as an instrument, we analyze the economic consequences of housing speculation during the U.S. housing boom in the 2000s. We find that housing speculation, anchored, in part, on extrapolation of past housing price changes, led not only to greater price appreciation, economic expansions, and housing construction during the boom in 2004–2006 but also to more severe economic downturns during the subsequent bust in 2007–2009. Our analysis supports supply overhang and local household demand as two key channels for transmitting these adverse effects.

The fundamentals of momentum investing: European evidence on understanding momentum through fundamentals

AbstractThis paper tests Ahmed and Safdar's noise‐related fundamentals‐based explanation for the momentum premium in European equity markets. Consistent with the view that past price changes may be partially driven by noise, the future return behaviour of winners and losers is significantly dependent upon the degree to which past price performance is consistent with fundamentals. European momentum profits are concentrated among those firms where past price performance is congruent with fundamentals, but absent among those firms where past price performance is incongruent with fundamentals. The significantly different momentum premiums on congruent and incongruent fundamentals‐momentum strategies are attributable to the exploitation of existing mispricing among momentum stocks that can be ex ante identified using firm fundamentals.

When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis

Motivated by the implication of prominent behavioral models that a joint consideration of past price changes and firm fundamentals could efficiently identify stock mispricing, we propose an integrated approach that combines fundamental and technical information. This integrated approach generates substantial economic gains, which are comparable to those of strategies double-sorted on characteristics related to high turnover and trading costs and state-of-the-art machine learning strategies in existing studies. The performance net of transaction costs is still attractive. Consistent with behavioral models, limits to arbitrage and information asymmetry play a significant role in explaining the super performance of this integrated approach.

Testing the random walk hypothesis of stock indexes through variance-ratio

The Random Walk is considered to be a tool trying to explain the characteristic of movement of prices in the financial markets. It can also be seen in the form of a trial to demonstrate the non-predictability of future changes in the financial markets through reliance on the characteristics identified based on past price changes. In this paper used is the variance-ratio test initiated by Lo and MacKinlay to test the Random Walk Hypothesis for a more recent data of eleven Stock Indexes, seen as main indexes of the current market.

Endogenous Liquidity Crises

Empirical data reveals that the liquidity flow into the order book (depositions, cancellations and market orders) is influenced by past price changes. In particular, we show that liquidity tends to decrease with the amplitude of past volatility and price trends. Such a feedback mechanism in turn increases the volatility, possibly leading to a liquidity crisis. Accounting for such effects within a stylized order book model, we demonstrate numerically that there exists a second order phase transition between a stable regime for weak feedback to an unstable regime for strong feedback,in which liquidity crises arise with probability one. We characterize the critical exponents, which appear to belong to a new universality class. We then propose a simpler model for spread dynamics that maps onto a linear Hawkes process which also exhibits liquidity crises. If relevant for the real markets, such a phase transition scenario requires the system to sit below, but very close to the instability threshold (self-organised criticality), or else that the feedback intensity is itself time dependent and occasionally visits the unstable region. An alternative scenario is provided by a class of non-linear Hawkes process that show occasional “activated” liquidity crises, without having to be poised at the edge of instability.

Modeling Investor Behavior Using Machine Learning: Mean‐Reversion and Momentum Trading Strategies

We model investor behavior by training machine learning techniques with financial data comprising more than 13,000 investors of a large bank in Brazil over 2016 to 2018. We take high‐frequency data on every sell or buy operation of these investors on a daily basis, allowing us to fully track these investment decisions over time. We then analyze whether these investment changes correlate with the IBOVESPA index. We find that investors decide their investment strategies using recent past price changes. There is some degree of heterogeneity in investment decisions. Overall, we find evidence of mean‐reverting investment strategies. We also find evidence that female investors and higher academic degree have a less pronounced mean‐reverting strategy behavior comparatively to male investors and those with lower academic degree. Finally, this paper provides a general methodological approach to mitigate potential biases arising from ad-hoc design decisions of discarding or introducing variables in empirical econometrics. For that, we use feature selection techniques from machine learning to identify relevant variables in an objective and concise way.

X-CAPM REVISITED: THE INSTITUTIONAL EXTRAPOLATIVE CAPITAL ASSET PRICING MODEL (I-X-CAPM)

This study constructs and tests a consumption-based asset pricing model in which some investors form beliefs about future price changes in the stock market by extrapolating past price changes, while other investors hold fully rational beliefs. The contribution of the present work is the inclusion of institutional investor bias. As such it extends theory. But it also conducts econometric tests by using daily survey data on individual and institutional investors’ sentiment on the current economic situation and their future expectations. Empirical findings may imply that institutions’ sentiment reverts quicker to the equilibrium price than individual sentiment, at least with regard to their beliefs on future economic outlook. If studied further with a bigger dataset, it may imply that institutional investors are closer to the rational-decision making mechanism compared to individual investors. The theoretical framework rests on prospect theory. The market studied is the US equity market, however findings and suggestions can be applied to global markets and various financial instruments.

Home Price Expectations and Behaviour: Evidence from a Randomized Information Experiment

AbstractHome price expectations are believed to play an important role in housing dynamics, yet we have limited understanding of how they are formed and how they affect behaviour. Using a unique “information experiment” embedded in an online survey, this article investigates how consumers’ home price expectations respond to past home price growth, and how they impact investment decisions. After eliciting respondents’ priors about past and future local home price changes, we present a random subset of them with factual information about past (one- or five-year) changes, and then re-elicit expectations. This unique “panel” data allows us to identify causal effects of the information, and provides insights on the expectation formation process. We find that, on average, year-ahead home price expectations are revised in a way consistent with short-term momentum in home price growth, though respondents tend to underpredict the strength of momentum. Revisions of longer-term expectations show that respondents do not expect the empirically-occurring mean reversion in home price growth. These patterns are in line with recent behavioural models of housing cycles. Finally, we show that home price expectations causally affect investment decisions in a portfolio choice experiment embedded in the survey.

Extrapolation and bubbles

We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals—an average of the asset’s past price changes and the asset’s degree of overvaluation—and “waver” over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles, that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model’s distinctive predictions.

X-CAPM REVISITED: THE INSTITUTIONAL EXTRAPOLATIVE CAPITAL ASSET PRICING MODEL (I-X-CAPM)

This study constructs and tests a consumption-based asset pricing model in which some investors form beliefs about future price changes in the stock market by extrapolating past price changes, while other investors hold fully rational beliefs. The contribution of the present work is the inclusion of institutional investor bias. As such it extends theory. But it also conducts econometric tests by using daily survey data on individual and institutional investors’ sentiment on the current economic situation and their future expectations. Empirical findings may imply that institutions’ sentiment reverts quicker to the equilibrium price than individual sentiment, at least with regard to their beliefs on future economic outlook. If studied further with a bigger dataset, it may imply that institutional investors are closer to the rational-decision making mechanism compared to individual investors. The theoretical framework rests on prospect theory. The market studied is the US equity market, however findings and suggestions can be applied to global markets and various financial instruments.

Models of Investor Forecasting Behavior — Experimental Evidence

Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.