Does trading volume really explain stock returns volatility?

This study investigates the relationship between trading volume and Bitcoin price movements using Pearson and Spearman correlation methods. The aim is to determine if trading volume can reliably predict Bitcoin price changes. Using a comprehensive dataset of daily Bitcoin prices and trading volumes, various statistical techniques were employed. Pearson and Spearman correlation analyses revealed very weak and statistically insignificant relationships, with correlation coefficients of -0.023788 and 0.021093, respectively. Linear regression analysis further supported these findings, showing an insignificant regression coefficient for trading volume and a very low R-squared value of 0.000566. Volatility analysis, measured by the standard deviation of daily returns, demonstrated high price volatility, consistent with the cryptocurrency market's nature. This volatility is influenced by factors such as market sentiment, regulatory developments, and macroeconomic events. The study also utilized 30-day moving averages to smooth short-term fluctuations and highlight long-term trends in trading volume and closing prices, revealing underlying trends not visible in daily data. A 1-day lagged correlation analysis indicated a very weak relationship (0.008145) between trading volume on one day and price changes on the next, suggesting other factors drive price movements. Visualizations, including time series graphs, histograms, moving averages, and volatility graphs, further illustrated the lack of a clear pattern between trading volume and price changes. In conclusion, trading volume is not a significant predictor of Bitcoin price movements, highlighting the need for comprehensive analytical approaches considering multiple variables to understand and predict Bitcoin price dynamics better.

is sometimes useful to view environmental quality as a "good" whose production requires real resources employed in environmental damage avoidance activities1. Transformation functions relating levels of environmental quality and production of conventional goods can then be constructed, and environmental control (EC) costs are viewed as opportunity costs measured in conventional goods forgone. In this spirit Walter has attempted to show, using a modified HeckscherOhlin model with three goods (exportables, importables, environmental damage avoidance), the effects of EC activities on the pattern and volume of international trade. He concludes inter alia that assuming constant terms of trade between X and M goods, and a proportionate shrinkage of the production frontier between X and M as a result of drawing resources away from conventional goods into environmental damage avoidance activities, the production and consumption of tradables will decrease and the volume of trade will decline. Further, he argues that if EC itself is a capital intensive activity, and if the country is capital abundant in the Heckscher-Ohlin sense, the transformation function between X and M will shrink proportionately more for the X good. This leads to a major reduction in the volume of trade, reduced specialization, and smaller gains from trade. His analysis is described in Diagram i showing the initial transformation frontier XM, the proportionately shrunk function X'M', and the skewed shrinkage X'M", and the respective trade triangles (constant consumption proportions of X and M assumed)2.

Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information serving as the mixing variable. The time series behavior of this mixing variable determines the dynamics of the price-volume system. In this article, bivariate mixture specifications with a serially correlated mixing variable are estimated by simulated maximum likelihood and analyzed concerning their ability to account for the observed dynamics on financial markets, especially the persistence in the variance of price changes. The results, based on German stock-market data, reveal that the dynamic bivariate mixture models cannot account for the persistence in the price-change variance.

We show that, to form aggregate inflation expectations, consumers rely on the price changes they face in their daily lives while grocery shopping.Specifically, the frequency and size of price changes, rather than their expenditure share, matter for individuals' inflation expectations.To document these facts, we collect novel micro data for a representative US sample that uniquely match individual expectations, detailed information about consumption bundles, and item-level prices.Our results suggest that the frequency and size of grocery-price changes to which consumers are personally exposed should be incorporated in models of expectations formation.Central banks' focus on core inflation---which excludes grocery prices---to design expectationsbased policies might lead to systematic mistakes.

Price Volatility, Trading Volume and Open Interest: Evidence from Indian Commodity Futures Markets

Bitcoin, launched in 2009 as the pioneer cryptocurrency, has caught the eye of investors worldwide due to its nature and remarkable growth. Recent studies indicate that elements, like announcements, news sentiments, government regulations, and overall market sentiments significantly influence the trajectory of bitcoin price. This work presents a comprehensive analysis to provide deeper insights into the relationship between user sentiments expressed as tweets and bitcoin prices (daily price changes). As a significant contribution, the authors propose multiple approaches to performing the non-trivial task of integrating tweet sentiment with bitcoin price data. The chapter also presents the incorporation of inherent lag in the expression of sentiments and their impact on price change by demonstrating lagged sentiment analysis. The work employs state-of-the-art machine learning and deep learning models for analyzing the data both as classification and regression tasks to uncover hidden patterns.

The South East Fishery (SEF), formerly known as the South East Trawl Fishery (SETF), is one of the oldest and most valuable commercial multi-species fisheries in Australia. On January 1, 1992, sixteen commercial species of the fishery were brought into a quota management system by introducing individual transferable quotas (ITQs). The non-homothetic Generalized Leontief revenue function was employed to estimate the vessel level supply functions of sixteen quota species in the fishery covering the period 1990-96. This approach was applied to investigate the technical and economic interactions among quota species. The supply model revealed some significant substitutability and complementarity relationships among quota species. For some species, the results also revealed significant spatial, inter-annual and seasonal variations in stock. In the next step, the revenue function framework was used to solve for a set of virtual prices for the sixteen quota species. The virtual prices, together with the observed market prices, are then used to estimate a set of quota prices. Predicted quota prices are compared with observed values, and the implications of various policy measures, including removing some species from the ITQ system, or removing some vessels from the fishery, are discussed. It was found that the set of species total allowable catch (TAC) limits imposed by the ITQ system is inconsistent with the production technology of the fishery. On the demand side, this study also analysed the process of price formation for the quota species in the Sydney Fish Market by using an inverse system framework. The log-linear version is supported by the data and the system was estimated using the seemingly unrelated regression (SUR) technique. In all cases own-price flexibility coefficients were observed to satisfy the theoretical expectation of the general price-quantity relations and their magnitudes revealed inflexibility of real wholesale prices to their corresponding quantities. Also, the majority of the quota species may be classified as normal goods. The impact of structural change in the fishery, as a result of the introduction of the quota system, on the prices of quota species was also examined and it was observed that the quota management system significantly affected the price of some of the quota species. This study also investigated the statistical time-series properties of individual price series to examine the long-run price relationships among quota species using the multivariate cointegration approach. It was found that 12 cointegrating vectors exist in the system at the 5% significance level. As the investigation of pair-wise relationships is complicated in the case of a system of 16 variables the impulse response function analysis was used to examine the interrelationships between the price variables. The results revealed some significant substitutability and complementarity relationships between price series. The weak exogeneity test results indicate that none of the price variables can be treated as weakly exogenous to the system. The short-run dynamic relationships among prices were also examined using the vector error-correction mechanism. It was found that changes in own lag prices are significant for thirteen cases. Hence any observed price changes for these cases cannot be attributed fully to the price changes in other species. It was also found that for twelve out of sixteen cases the changes in the price of a particular species was significantly responsive to the changes in the price of at least one other species. Seasonal components were apparent in the majority of the price series. As a part of time series modelling, this study also investigated the autoregressive conditional heteroscedasticity (ARCH) and generalized-ARCH (GARCH) effects in the price series. It was found that in all cases significant ARCH and /or GARCH effects were present. To search for the origins of these effects a weakly exogenous variable (trading volume) was introduced to the conditional variance equation of the ARCH and GARCH models, provided that such effects were observed in the first stage of investigation. It was found that in fourteen cases the estimated coefficients of the trading volume were negative. In all cases, the trading quantity variable did not contribute to the removal of the ARCH and/or GARCH effects. Finally, the policy implications of the findings are discussed.

Commonality, Information and Return/Return Volatility - Volume Relationship

Tests for efficiency provide one benchmark against which the informational efficiency of a market may be judged. This article reports the results of weak-form efficiency tests of the London market for 3 percent consols for the period 1821-60, using approximately 10,000 daily price changes. The results are similar to those found in contemporary markets, suggesting that this market was efficient in the sense defined. Copyright 1989 by The London School of Economics and Political Science.

Multifractal analysis of managed and independent float exchange rates

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a wisdom-of-the-crowd effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the sentiment.

As social networks are becoming more and more popular, more and more data are available from them. Researchers are now trying to extract useful information from these big data. One possible usage of social media is to investigate stock market. There are two major events to measure in stock market - price change and trade volume. In this paper, we firstly use our trust framework to build up trust network among Twitter users in a stock market group. We compare trust information extracted from Twitter group with Dow Jones Industrial Average (DJIA) get from Yahoo! Finance. Our results show that by taking trust information into account, they are more correlated than just counting the number of tweets. Also it shows us that trade volume is stronger correlated than price change.

I investigate the impact of daily oil price changes on the stock returns of a wide array of industries. I find that in addition to the stock returns of industries that depend heavily on oil, stock returns of some industries that use little oil also are sensitive to oil prices perhaps because their main customers are impacted by oil price changes. In addition, I present robust estimates of industries’ cost‐side and demand‐side dependence on oil. These measures can serve as reliable benchmarks when classifying industries into oil‐intensive and non–oil‐intensive groups, a distinction widely used in studies and media without any quantitative justification so far. Further, I find that the sensitivity of industries’ returns to oil price changes depends on both the cost‐side and demand‐side dependence on oil and that the relative effects of these factors vary across industries.

Researchers have used stylized facts on asset prices and trading volume in stock markets (in particular, the mean reversion of asset returns and the correlations between trading volume, price changes and price levels) to support theories where agents are not rational expected utility maximizers. This paper shows that this empirical evidence is in fact consistent with a standard infinite horizon - perfect information - expected utility economy where some agents face leverage constraints similar to those found in todays financial markets. In addition, and in sharp contrast to the theories above, we explain some qualitative differences that are observed in the price-volume relation on stock and on futures markets. We consider a continuous-time economy where agents maximize the integral of their discounted utility from consumption under both budget and leverage constraints. Building on the work by Vila and Zariphopoulou (1997), we find a closed form solution, up to a negative constant, for the equilibrium prices and demands in the region of the state space where the constraint is non-binding. We show that, at the equilibrium, stock holdings volatility as well as its ratio to stock price volatility are increasing functions of the stock price and interpret this finding in terms of the price-volume relation.

Trading volume changes based on market microstructure will impact asset prices, which will lead to transaction price changes. Based on the extended Hasbrouck–Foster–Viswanathan (HFV) model, we study the statistical characteristics of daily permanent price impact and daily temporary price impact using high-frequency data from Chinese Stock Markets. We estimate this model using tick-by-tick data for 16 selected stocks that are traded on the Shanghai Stock Exchange. We find the following: (1) the time series of both the permanent price impact and temporary price impact exist in stationarity and long-term memory; (2) there is a strong correlation between the permanent price impact among assets, while the correlation coefficient of the temporary price impact is generally weak; (3) the time interval has no significant influence on the trade volume and the price change at the tick frequency, which means that it is not necessary to take into account the time interval between adjacent transaction in high-frequency trading; and (4) the bid-ask spread is an effective factor to explain trading price change, but has no significant impact on trade volume.