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Race for speed among algo traders hits peak
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‘Alt’ Data in Emerging Markets
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How to Create an ARIMA Model for Time Series Forecasting with Python
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The relationship between trading frequency and achievable alpha
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‘Crowdsourced’ Quantopian dives into algorithms
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### Trending academic research:

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Most popular SSRN papers
Forecasting Stock Returns with Large Dimensional Factor Models
Dynamical Analysis of Stock Market Instability by Cross-correlation Matrix
abstract We study stock market instability by using cross-correlations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the cross-correlations, cross-correlation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally de-localized when the market is volatile.
The Classification of Stocks with Basic Financial Indicators: An Application of Cluster Analysis on the BIST 100 Index
Optimal client recommendation for market makers in illiquid financial products
abstract The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.
High-Frequency Jump Analysis of the Bitcoin Market
abstract We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short-term positive impact on market activity and illiquidity and see a persistent change in the price.
An Analysis on the Structural Breaks in Dynamic Conditional Correlations Among Equity Markets Based on the ICSS Algorithm: The Case from 2015-2016 Chinese Stock Market Turmoil
Stability of zero-growth economics analysed with a Minskyan model
abstract As humanity is becoming increasingly confronted by Earth's finite biophysical limits, there is increasing interest in questions about the stability and equitability of a zero-growth capitalist economy, most notably: if one maintains a positive interest rate for loans, can a zero-growth economy be stable? This question has been explored on a few different macroeconomic models, and both `yes' and `no' answers have been obtained. However, economies can become unstable whether or not there is ongoing underlying growth in productivity with which to sustain growth in output. Here we attempt, for the first time, to assess via a model the relative stability of growth versus no-growth scenarios. The model employed draws from Keen's model of the Minsky financial instability hypothesis. The analysis focuses on dynamics as opposed to equilibrium, and scenarios of growth and no-growth of output (GDP) are obtained by tweaking a productivity growth input parameter. We confirm that, with or without growth, there can be both stable and unstable scenarios. To maintain stability, firms must not change their debt levels or target debt levels too quickly. Further, according to the model, the wages share is higher for zero-growth scenarios, although there are more frequent substantial drops in employment.
Generating Options-Implied Probability Densities to Understand Oil Market Events
The Information Content of Option Implied Moments and Co-Moments: Extended Abstract
Transaction Costs and Liquidity Co-Movements within and Across Exchanges: Evidence from Vietnam
Stock Price Management and Share Issuance: Evidence from Equity Warrants
News, Noise, and Tests of Present Value Models
Estimation of the Discontinuous Leverage Effect: Evidence from the NASDAQ Order Book
Cognitive Biases that Interfere with Critical Thinking and Scientific Reasoning: A Course Module
Pairs Trading under Drift Uncertainty and Risk Penalization
abstract In this work, we study the dynamic portfolio optimization problem related to the pairs trading, which is an investment strategy that matches a long position in one security with a short position in an another security with similar characteristics. The relation between pairs, called spread, is modeled by a Gaussian mean-reverting process whose drift rate is modulated by an unobservable continuous-time finite state Markov chain. Using the classical stochastic filtering theory, we reduce this problem with partial information to the one with complete information and solve it for the logarithmic utility function, where the terminal wealth is penalized by the riskiness of the portfolio according to the realized volatility of the wealth process. We characterize optimal dollar-neutral strategies as well as optimal value functions under both full and partial information and show that the certainty principle holds for the optimal portfolio strategy. Finally, we provide a numerical analysis for a simple example with a two-state Markov chain.
What Kind of Earnings Shape More Market Expectations?
Information Aggregation in Dynamic Markets with Adverse Selection
The Systematic Risk of Short-Sale Restrictions
Learning Agents in Black-Scholes Financial Markets: Consensus Dynamics and Volatility Smiles
abstract Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in practice it varies. How do traders come to learn these parameters? We introduce natural models of learning agents, in which they update their beliefs about the true implied volatility based on the opinions of other traders. We prove convergence of these opinion dynamics using techniques from control theory and leader-follower models, thus providing a resolution between theory and market practices. We allow for two different models, one with feedback and one with an unknown leader and no feedback. Both scalar and multidimensional cases are analyzed.
Should the U.S. Government Issue Floating Rate Notes?
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