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A Multifactor Model of Credit Spreads

Ramaprasad Bhar, Nedim Handzic
2008 Social Science Research Network  
We use a state-space model to represent the time-variation of credit spread indices by rating as a function of latent factors of the Vasicek form.  ...  equation (3) maps the vector of observed credit spreads R t (n × 1) to the state vector X t (m × 1) via a measurement matrix Z(ψ)(n × m).  ...  At each time step t, the filtered estimateX t consists of a predictive componentX t|t−1 , based on information to time t − 1, and an updating component incorporating new observations from R t .  ... 
doi:10.2139/ssrn.1263173 fatcat:bprktljj5zgndav475h5i2d2xy

A Multifactor Model of Credit Spreads

Ramaprasad Bhar, Nedim Handzic
2008 Social Science Research Network  
We use a state-space model to represent the time-variation of credit spread indices by rating as a function of latent factors of the Vasicek form.  ...  equation (3) maps the vector of observed credit spreads R t (n × 1) to the state vector X t (m × 1) via a measurement matrix Z(ψ)(n × m).  ...  At each time step t, the filtered estimateX t consists of a predictive componentX t|t−1 , based on information to time t − 1, and an updating component incorporating new observations from R t .  ... 
doi:10.2139/ssrn.1156311 fatcat:of5ypynxjjas7j7rqsihysdkgu

International Equity Flows and Returns: A Quantitative Equilibrium Approach

RUI ALBUQUERQUE, GREGORY H. BAUER, MARTIN SCHNEIDER
2007 The Review of Economic Studies  
Americans build and unwind foreign equity positions gradually and (iii) US investors increase their market share in a country when stock prices there have recently been rising.  ...  It presents a quantitative model of trading that is built around two new assumptions: (i) both the foreign and domestic investor populations contain investors of different sophistication, and (ii) investor  ...  The risk premium increases via the myopic demand because of the substitutability across assets and via a lower hedging demand because sophisticated investors' exposure to these state variables (which are  ... 
doi:10.1111/j.1467-937x.2007.00412.x fatcat:7dcseqxkuzfqbgdfs5nx7b3z5i

A Multifactor Model of Credit Spreads

Ramaprasad Bhar, Nedim Handzic
2010 Asia-Pacific Financial Markets  
We use a state-space model to represent the time-variation of credit spread indices by rating as a function of latent factors of the Vasicek form.  ...  equation (3) maps the vector of observed credit spreads R t (n × 1) to the state vector X t (m × 1) via a measurement matrix Z(ψ)(n × m).  ...  At each time step t, the filtered estimateX t consists of a predictive componentX t|t−1 , based on information to time t − 1, and an updating component incorporating new observations from R t .  ... 
doi:10.1007/s10690-010-9123-3 fatcat:55jadegaofdwlehqddokfto3pu

Stock Market Analysis with Text Data: A Review [article]

Kamaladdin Fataliyev, Aneesh Chivukula, Mukesh Prasad, Wei Liu
2021 arXiv   pre-print
The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions  ...  Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions.  ...  As stated in Section 4, statistical and machine learning methods are widely used for stock market prediction.  ... 
arXiv:2106.12985v2 fatcat:prfo5c6bmfd3piwpl5yevfycje

On pricing kernels, information and risk

Diane L. Wilcox, Tim J. Gebbie
2015 Investment Analysts Journal  
returns of stocks listed on the JSE.  ...  the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity  ...  Models for equity returns and risk premia The idea that NA drives pricing is coupled to the perspective that risk factors exogenous to the market can move prices and that news arrives often and randomly  ... 
doi:10.1080/10293523.2014.994437 fatcat:oa7orrp7mjeq3ajbvpdlxmpucu

Predictive Market Making via Machine Learning

Abbas Haider, Hui Wang, Bryan Scotney, Glenn Hawe
2022 SN Operations Research Forum  
Important questions are, how to predict stock price movement, and how to utilise such prediction?  ...  Our PMM method is evaluated against the state-of-the-art (RL-based MM) and a traditional MM method, using ten stocks and three exchange traded funds (ETFs).  ...  The curves shown in Fig. 4 look noisy, and therefore a direct conclusion is difficult to be drawn. Hence, we use averaged return value of each equity of all strategies.  ... 
doi:10.1007/s43069-022-00124-0 fatcat:geagrdxaebeafgpiaztfajaec4

Market Expectations in the Cross-Section of Present Values

BRYAN KELLY, SETH PRUITT
2013 Journal of Finance  
Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable.  ...  This is not the case when the crosssection involves individual stocks.  ...  result that value stocks have high cash flow news betas.  ... 
doi:10.1111/jofi.12060 fatcat:5g2jf5jrivfmzk3fpgnkwchbiy

Market Expectations in the Cross Section of Present Values

Bryan T. Kelly, Seth Pruitt
2012 Social Science Research Network  
Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable.  ...  This is not the case when the crosssection involves individual stocks.  ...  result that value stocks have high cash flow news betas.  ... 
doi:10.2139/ssrn.1752543 fatcat:qo72ykpdlbejjam6skevyfbqk4

Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods

Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
2018 Journal of Forecasting  
We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series  ...  A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies.  ...  data. 20 The choice is driven by the necessity of having a sufficient amount of data for training (this excludes illiquid stocks) while covering different industry sectors.  ... 
doi:10.1002/for.2543 fatcat:mwlf3efxwzgyrogbii7ygmmgdy

Universal features of price formation in financial markets: perspectives from Deep Learning [article]

Justin Sirignano, Rama Cont
2018 arXiv   pre-print
The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock  ...  The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors.  ...  Each LSTM unit has an internal state which maintains a nonlinear representation of all past data. This internal state is updated as new data arrives.  ... 
arXiv:1803.06917v1 fatcat:plodnqudnzfk5ggrtuxioewzfm

Universal features of price formation in financial markets: perspectives from deep learning

Justin Sirignano, Rama Cont
2019 Quantitative finance (Print)  
The universal model -trained on data from all stocks -outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing  ...  The universal price formation model exhibits a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors.  ...  Each LSTM unit has an internal state which maintains a nonlinear representation of all past data. This internal state is updated as new data arrives.  ... 
doi:10.1080/14697688.2019.1622295 fatcat:hs4sab5xrngrvmn4f2w64p2o7e

A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Mr. Yash Kadam, Mr. Sujay Kulkarni, Mr. Suyog Lonsane, Prof. Anjali S. Khandagale
2022 International Journal for Research in Applied Science and Engineering Technology  
This paper explains the prediction of a stock using Machine Learning.  ...  Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange.  ...  The efficient market hypothesis states that it is not possible to predict stock prices and that stock behaves in the random walk.  ... 
doi:10.22214/ijraset.2022.40635 fatcat:ba57sntpc5hftd7x6ixtltukve

DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies [article]

Pok Wah Chan
2022 arXiv   pre-print
The framework is evaluated on two self-annotated financial anomalies, i.e., Twitter and Facebook stock price on 29 and 30 April 2021.  ...  Extreme pricing anomalies may occur unexpectedly without a trivial cause, and equity traders typically experience a meticulous process to source disparate information and analyze its reliability before  ...  These potential equity price driven forces are not captured by any attribute that can be quantified in the CAPM model.  ... 
arXiv:2203.08144v1 fatcat:rusadj2rgrbplekl3bbpunbdwm

Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes [article]

Sid Ghoshal, Stephen Roberts
2018 arXiv   pre-print
Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data.  ...  In particular our findings highlight the critical value of options data in mapping out the curvature of price space and inspire an intuitive, novel direction for research in financial prediction.  ...  Technicals-driven Gaussian Process regression has been applied to forecasting time-series in a wide range of asset classes, including stock market prices (Farrell and Correa, 2007) , stock market volatility  ... 
arXiv:1603.06202v2 fatcat:na3roeg43neltgpry36yuoyaui
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