Forecasting The Intraday Trading Volume Of Stocks PDF Download
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Author | : Alireza Fallahi |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : Autoregression (Statistics) |
ISBN | : |
Download Forecasting the Intraday Trading Volume of Stocks Book in PDF, ePub and Kindle
Author | : Ran Chen |
Publisher | : |
Total Pages | : 16 |
Release | : 2018 |
Genre | : |
ISBN | : |
Download Forecasting Intraday Trading Volume Book in PDF, ePub and Kindle
An accurate forecast of intraday volume is a key aspect of algorithmic trading. This manuscript proposes a state-space model to forecast intraday trading volume via the Kalman filter and derives closed-form expectation-maximization (EM) solutions for model calibration. The model is extended to handle outliers in real-time market data by applying a sparse regularization technique. Empirical studies using thirty securities on eight exchanges show that the proposed model substantially outperforms the rolling means (RM) and the state-of-the-art Component Multiplicative Error Model (CMEM) by 64% and 29%, respectively, in volume prediction and by 15% and 9%, respectively, in Volume Weighted Average Price (VWAP) trading.
Author | : Ana-Maria Fuertes |
Publisher | : |
Total Pages | : 72 |
Release | : 2013 |
Genre | : |
ISBN | : |
Download Forecasting Daily Stock Volatility Book in PDF, ePub and Kindle
Several recent studies advocate the use of nonparametric estimators of daily price variability that exploit intraday information. This paper compares four such estimators, realised volatility, realised range, realised power variation and realised bipower variation, by examining their in-sample distributional properties and out-of-sample forecast ranking when the object of interest is the conventional conditional variance. The analysis is based on a 7-year sample of transaction prices for 14 NYSE stocks. The forecast race is conducted in a GARCH framework and relies on several loss functions. The realized range fares relatively well in the in-sample fit analysis, for instance, regarding the extent to which it brings normality in returns. However, overall the realised power variation provides the most accurate 1-day-ahead forecasts. Forecast combination of all four intraday measures produces the smallest forecast errors in about half of the sampled stocks. A market conditions analysis reveals that the additional use of intraday data on day t-1 to forecast volatility on day t is most advantageous when day t is a low volume or an up-market day. The results have implications for value-at-risk analysis.
Author | : Luc Bauwens |
Publisher | : Springer Science & Business Media |
Total Pages | : 192 |
Release | : 2013-11-11 |
Genre | : Business & Economics |
ISBN | : 147573381X |
Download Econometric Modelling of Stock Market Intraday Activity Book in PDF, ePub and Kindle
Over the past 25 years, applied econometrics has undergone tremen dous changes, with active developments in fields of research such as time series, labor econometrics, financial econometrics and simulation based methods. Time series analysis has been an active field of research since the seminal work by Box and Jenkins (1976), who introduced a gen eral framework in which time series can be analyzed. In the world of financial econometrics and the application of time series techniques, the ARCH model of Engle (1982) has shifted the focus from the modelling of the process in itself to the modelling of the volatility of the process. In less than 15 years, it has become one of the most successful fields of 1 applied econometric research with hundreds of published papers. As an alternative to the ARCH modelling of the volatility, Taylor (1986) intro duced the stochastic volatility model, whose features are quite similar to the ARCH specification but which involves an unobserved or latent component for the volatility. While being more difficult to estimate than usual GARCH models, stochastic volatility models have found numerous applications in the modelling of volatility and more particularly in the econometric part of option pricing formulas. Although modelling volatil ity is one of the best known examples of applied financial econometrics, other topics (factor models, present value relationships, term structure 2 models) were also successfully tackled.
Author | : Anna Coulling |
Publisher | : Marinablu International Ltd |
Total Pages | : 336 |
Release | : 2023-11-30 |
Genre | : Business & Economics |
ISBN | : |
Download Stock Trading & Investing Using Volume Price Analysis Book in PDF, ePub and Kindle
It was good enough for them What do Charles Dow, Jesse Livermore, and Richard Ney have in common? They used volume and price to anticipate where the market was heading next, and so built their vast fortunes. For them, it was the ticker tape, for us it is the trading screen. The results are the same and can be for you too. You can be lucky too I make no bones about the fact I believe I was lucky in starting my own trading journey using volume. To me it just made sense. The logic was inescapable. And for me, the most powerful reason is very simple. Volume is a rare commodity in trading - a leading indicator. The second and only other leading indicator is price. Everything else is lagged. It's a simple problem As traders, investors or speculators, all we are trying to do is to forecast where the market is heading next. Is there any better way than to use the only two leading indicators we have at our disposal, namely volume and price? And such a powerful solution In isolation, each tells us very little. After all, volume is just that, no more no less. A price is a price. However, combine these two forces together, and the result is a powerful analytical approach to confidently forecasting market direction. What you will discover This book takes all the principles from A Complete Guide To Volume Price Analysis and applies them across all the timeframes with over 200 worked examples, all annotated and with a full explanation of the key lessons. So whether you're a day trader or longer-term investor, this book is the perfect platform to set you on the road to success and join those iconic traders of the past.
Author | : Joe Lange |
Publisher | : |
Total Pages | : 24 |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Measuring, Forecasting and Explaining Time Varying Liquidity in the Stock Market Book in PDF, ePub and Kindle
The paper proposes a new measure of market liquidity, VNET, which directly measures the depth of the market. VNET is constructed from the excess volume of buys or sells during a market event defined by a price movement. As this measure varies over time, it can be forecast and explained. Using NYSE TORQ data, it is found that market depth varies positively but less than proportionally with past volume and negatively with the number of transactions. Both findings suggest that over the day high volumes are associated with an influx of informed traders and reduce market liquidity. The timing of events plays an intimate role in the analysis. High expected volatility as measured by the ACD model of Engle and Russell (1997) reduces expected liquidity. Finally, market depth is smaller when the one-sided trading volume is transacted in a shorter than expected time, providing an estimate of the value of patience.
Author | : Joseph E. Granville |
Publisher | : Pickle Partners Publishing |
Total Pages | : 531 |
Release | : 2018-12-05 |
Genre | : Business & Economics |
ISBN | : 1789126037 |
Download Granville’s New Key to Stock Market Profits Book in PDF, ePub and Kindle
In this remarkable stock market study, one of Wall Street’s best known market analysts reveals a new technical tool he developed for gauging the pulse of the trading cycle. Called the On Balance Volume Theory, this tool tends to fill in some of the conspicuous voids in the famous Dow Theory—especially the lack of discussion and use of stock volume figures. As straightforward as a set of bridge rules, on-balance volume (OBV) denotes each buy and sell signal so that a trader can follow them without his own emotions tending to lead him astray—emotions causing most of the market misjudgements that take place. The Granville OBV method is essentially scientific, has a high degree of accuracy and has many automatic features. The reader of this book will be introduced to a method whereby he may benefit by the earlier movements of volume over price—the “early warning” radar of volume buy and sell signals.
Author | : Pierre Cattin |
Publisher | : |
Total Pages | : |
Release | : 2018 |
Genre | : |
ISBN | : |
Download Predicting Intraday Stock Price Direction Using Machine Learning Techniques Book in PDF, ePub and Kindle
This thesis explores the price predictability of six large American stocks. Forecasts of price direction over four horizons from one minute up to one hour are performed. We create the predictors using past trades that occurred in the two hours preceding a prediction. One approach uses return and volume series as predictors and the second approach uses technical indicators. The classification algorithms are Logistic Regression, k-Nearest Neighbours, Random Forest, Feedforward Neural Network, and a Heterogeneous Ensemble combining these four models. We tune the models' hyperparameters using Bayesian optimization. The predictions of the ensemble are used in an intraday trading simulation that beats a passive strategy in terms of both profit and risk. However, the strategy does not remain profitable after taking transaction fees into account.
Author | : Melissa Danielle Davis |
Publisher | : |
Total Pages | : 44 |
Release | : 2000 |
Genre | : |
ISBN | : |
Download Volume and Volatility in the Stock Market Book in PDF, ePub and Kindle
Author | : Stephen Satchell |
Publisher | : Elsevier |
Total Pages | : 428 |
Release | : 2011-02-24 |
Genre | : Business & Economics |
ISBN | : 0080471420 |
Download Forecasting Volatility in the Financial Markets Book in PDF, ePub and Kindle
Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling