共查询到20条相似文献，搜索用时 15 毫秒
1.
Aubrey Clayton 《Quantitative Finance》2013,13(11):1771-1774
Taleb [Election predictions as martingales: An arbitrage approach. Quant. Finance, 2018, 18, 1–5] claimed a novel approach to evaluating the quality of probabilistic election forecasts via no-arbitrage pricing techniques and argued that popular forecasts of the 2016 U.S. Presidential election had violated arbitrage boundaries. We show that under mild assumptions all such political forecasts are arbitrage-free and that the heuristic that Taleb's argument was based on is false. 相似文献
2.
In this paper we propose an artificial market where multiple risky assets are exchanged. Agents are constrained by the availability of resources and trade to adjust their portfolio according to an exogenously given target portfolio. We model the trading mechanism as a continuous auction order-driven market. Agents are heterogeneous in terms of desired target portfolio allocations, but they are homogeneous in terms of trading strategies. We investigate the role played by the trading mechanism in affecting the dynamics of prices, trading volume and volatility. We show that the institutional setting of a double auction market is sufficient to generate a non-normal distribution of price changes and temporal patterns that resemble those observed in real markets. Moreover, we highlight the role played by the interaction between individual wealth constraints and the market frictions associated with a double auction system to determine the negative asymmetry of the stock returns distribution. 相似文献
3.
Most of the existing technical trading rules are linear in nature. This paper investigates the predictability of nonlinear time series model based trading strategies in the U.S. stock market. The performance of the nonlinear trading rule is compared with that of the linear model based rules. It is found that the self-exciting threshold autoregressive (SETAR) model based trading rules perform slightly better than the AR rules for the Dow Jones and Standard and Poor 500, while the AR rules perform slightly better in the NASDAQ market. Both the SETAR and the AR rules outperform the VMA rules. The results are confirmed by bootstrap simulations. 相似文献
4.
采用两只股票的日数据和5种高频数据,借鉴组合预测思想,综合利用协整模型和新卡尔曼滤波模型,与统计套利策略具体目标相结合,设计出新统计套利组合策略,实证分析数据频率、策略选择对统计套利效果的影响.结果表明:运用高频数据及引入卡尔曼滤波模型均有效,但卡尔曼滤波模型与协整模型不存在明显优劣之分,选择组合策略是必要的;组合策略收益性显著优于采取单一模型的套利策略;组合策略下的套利组合随数据频率提高,收益率波动性更小、更稳定;组合策略接近市场中性,能很好地免疫市场风险. 相似文献
5.
Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches. 相似文献
6.
Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our analysis is based on the Creditreform database. In all tests performed in this paper the nonlinear model classified by SVM exceeds the benchmark logit model, based on the same predictors, in terms of the performance metric, AR. The empirical evidence is in favor of the SVM for classification, especially in the linear non-separable case. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of SVM parameters. In terms of the empirical results obtained by SVM, the eight most important predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Some of the financial ratios selected by the SVM model are new because they have a strong nonlinear dependence on the default risk but a weak linear dependence that therefore cannot be captured by the usual linear models such as the DA and logit models. 相似文献
7.
This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations. 相似文献
8.
Statistical time-series approaches to hedging are difficult to beat, especially out-of-sample, and are capable of out-performing many theory-based derivative pricing model approaches to hedging commodity price risks using futures contracts. However, the vast majority of time-series approaches to hedging discussed in the literature are essentially linear statistical projections, whether univariate or multivariate. Little is known about the potential hedging capabilities of nonlinear methods. This study describes how least-squares orthogonal polynomial approximation methods based on the spanning polynomial projection (SPP) can be used to enhance standard (linear) optimal hedging methods and improve hedging performance for a hedger with a mean–variance objective. Empirical analyses show that the SPP can be used effectively for hedging and gives better out-of-sample hedging performance than the benchmark VEC-GARCH hedging model. Results are robust to the inclusion of transaction costs and risk-aversion assumptions. 相似文献
9.
Kshitij Kakade Aswini Kumar Mishra Kshitish Ghate Shivang Gupta 《International Journal of Intelligent Systems in Accounting, Finance & Management》2022,29(2):103-117
This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests. 相似文献
10.
Anthony S. Tay Christopher Ting Yiu Kuen Tse Mitch Warachka 《Quantitative Finance》2013,13(3):447-457
We explore the role of trade volume, trade direction, and the duration between trades in explaining price dynamics and volatility using an Asymmetric Autoregressive Conditional Duration model applied to intraday transactions data. Our results suggest that volume, direction and duration are important determinants of price dynamics, while duration is also an important determinant of volatility. However, the impact of volume and direction on volatility is marginal after controlling for duration, and the impact of volume on volatility appears to be confined to periods of infrequent trading. 相似文献
11.
Financial decision-making problems based on relatively few observations and several explanatory variables can be problematic for the common machine learning (ML) tools, since they cannot efficiently discriminate the relevant information. To investigate the challenges of this “small data” regime, we employ several state-of-the-art ML methods for predicting whether three selected stocks from the Swiss Market Index will outperform the market, by using, as classification features, a set of commonly used technical indicators. We show that the recently introduced entropic Scalable Probabilistic Approximation (eSPA) algorithm significantly surpasses its competitors in both prediction accuracy and computational cost. We then discuss the interpretability of the employed ML methods and suggest some statistically derived heuristics to select the most appropriate and parsimonious financial decision-making candidate model. 相似文献
12.
We investigate the feasibility of machine learning methods for attributional content and framing analysis in corporate reporting. We test the performance of five widely-used supervised machine learning classifiers (naïve Bayes, logistic regression, support vector machines, random forests, decision trees) in a top-down three-level hierarchical setting to (1) identify performance-related statements; (2) detect attributions in these; and (3) classify the content of the attributional statements. The training set comprises manually coded statements from a corpus of management commentary reports of listed companies. The attributions include both intra- and inter-sentential attributional statements. The results show that for both intra- and inter-sentential attributions, F1-scores of our most accurate classifier (i.e., support vector machines) vary in the range of 76% up to 94%, depending on the identification, detection and classification levels and the content characteristics of attributions. Additionally, we assess the hierarchical performance of classifiers, providing insights into a more holistic classification process for attributional statements. Overall, our results show how machine learning methods may facilitate narrative disclosure analysis by providing a more efficient way to detect and classify performance-related attributional statements. Our findings contribute to the accounting and management literature by providing a basis for implementing machine learning methodologies for research investigating attributional behavior and related impression management. 相似文献
13.
We revisit the problem of calculating the exact distribution of optimal investments in a mean variance world under multivariate normality. The context we consider is where problems in optimisation are addressed through the use of Monte-Carlo simulation. Our findings give clear insight as to when Monte-Carlo simulation will, and will not work. Whilst a number of authors have considered aspects of this exact problem before, we extend the problem by considering the problem of an investor who wishes to maximise quadratic utility defined in terms of alpha and tracking errors. The results derived allow some exact and numerical analysis. Furthermore, they allow us to also derive results for the more traditional non-benchmarked portfolio problem. 相似文献
14.
Christopher Hessel 《Quantitative Finance》2013,13(5):545-554
This paper investigates the changes in credit spread volatility during 1993–2001. We find that the credit spreads between junk-grade corporate bonds and Treasury bonds were significantly more volatile in the second half of this period when credit-related securities became popular. In contrast, investment-grade bonds exhibited no significant change in volatility. The junk bonds variance ratios changed from being less than one to greater than one. Using the GJR-Garch model, the conditional volatilities of junk bonds increased in the second half of the period and the mean reversion speeds slowed, suggesting a longer time for mean reversion to occur. Our analysis rules out treasury volatility, credit spread level, equity market return, T-bill rate, curvature of the Treasury curve, financial crisis, quantity of defaults and standard deviation of defaults as explanations for the increase in junk bond volatility. In contrast, volatility of equity returns provides a partial explanation of junk bond spread volatility in the later period. 相似文献
15.
The present study, based on data for delisted and active corporations in the Australian materials industry, is an attempt to develop a systematic way of selecting corporate failure‐related features. We empirically tested the proposed procedure using three datasets. The first dataset contains 82 financial economic factors from the corporation's financial statement. The second dataset comprises 73 relevant financial ratios, which either directly or indirectly measure a corporation's propensity to fail, and are conciliated from the first dataset. The third dataset is a parsimonious dataset obtained from the application of combining a filter and a wrapper to preprocess the first dataset. The robustness of this preprocessed dataset is tested by comparing its performance with the first and second datasets in two statistical (logistic regression and naïve‐Bayes) and two machine learning (decision tree, neural network) classes of prediction models. Tests for prediction accuracies and reliabilities, using the computational (ROC curve, AUC) and the statistical (Cochran's Q statistic) criteria show that the third dataset outperforms the other two datasets in all four predicting models, achieving various accuracies ranges from 81 per cent to 84 per cent. 相似文献
16.
探析高校教学设计的发展方向 总被引：1，自引：0，他引：1
随着社会对高校培养的人才素质要求的提高和教学手段的多样化,学习理论发生了一些新变化,认知学派虽然保持了在教学设计中占优势的地位,但人们对于一味的强调认知而忽视情感并不满意,人本主义因此受到了前所未有的青睐,新崛起的建构主义对学习理论进行了革新,代表了学习理论的发展方向;学习理论是教学设计的心理学基础,它的变革必然导致教学设计向前发展。 相似文献
17.
Sonja Huber 《Quantitative Finance》2013,13(8):871-882
The quality of operational risk data sets suffers from missing or contaminated data points. This may lead to implausible characteristics of the estimates. Outliers, especially, can make a modeler's task difficult and can result in arbitrarily large capital charges. Robust statistics provides ways to deal with these problems as well as measures for the reliability of estimators. We show that using maximum likelihood estimation can be misleading and unreliable assuming typical operational risk severity distributions. The robustness of the estimators for the Generalized Pareto distribution, and the Weibull and Lognormal distributions is measured considering both global and local reliability, which are represented by the breakdown point and the influence function of the estimate. 相似文献
18.
Over the past 15 years, there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorization Machines, respectively, to this end. However, these approaches either completely neglect interactions between the features extracted from the text, or they only account for second-order interactions. In this paper, we apply higher-order Factorization Machines, for which efficient training algorithms have only been available since 2016. As Factorization Machines require hyperparameters to be specified, we also introduce a novel adaptive-order algorithm for automatically determining them. Our study is the first one to make use of social media data for predicting minute-by-minute stock returns, namely the ones of the S&P 500 stock constituents. We show that, unlike a trading strategy employing support vector machines, Factorization-Machine-based strategies attain positive returns after transactions costs for the years 2014 and 2015. Especially the approach applying the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria, and it features very favorable characteristics. 相似文献
19.
Zdzisław Burda Andrzej Jarosz Maciej A. Nowak Jerzy Jurkiewicz Gábor Papp Ismail Zahed 《Quantitative Finance》2013,13(7):1103-1124
We apply the concept of free random variables to doubly correlated (Gaussian) Wishart random matrix models, appearing, for example, in a multivariate analysis of financial time series, and displaying both inter-asset cross-covariances and temporal auto-covariances. We give a comprehensive introduction to the rich financial reality behind such models. We explain in an elementary way the main techniques of free random variables calculus, with a view to promoting them in the quantitative finance community. We apply our findings to tackle several financially relevant problems, such as a universe of assets displaying exponentially decaying temporal covariances, or the exponentially weighted moving average, both with an arbitrary structure of cross-covariances. 相似文献
20.