Price Discovery Research Articles

Margin-buying, short-selling, and stock valuation: Why is the effect reversed over time in China?

China launched a pilot program in March 2010 to lift the ban on margin-buying and short-selling. Based on the first two batches of designated stocks, the literature documents that lifting the ban has a negative effect on stock valuation. However, we show that the effect has reversed to positive for the next six batches of designated stocks. We explore several potential explanations. Our analyses show that as short-selling volume grew at a much slower pace or even declined relative to margin-buying volume over our sample period, the positive effect of margin-buying on stock valuation has dominated the negative effect of short-selling. Both time-series and cross-sectional tests show that the imbalance between margin-buying and short-selling is the main driver of the reversal. We further show that the effect of lifting margin-buying and short-selling ban on stock price efficiency and discovery also reversed over time.

A Generative AI Approach to Pricing Mechanisms and Consumer Behavior in the Electric Vehicle Charging Market

The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.

The Relationship between SOFR and Market Yield of U.S. Treasury Securities

This paper introduces the characteristic of the new benchmark rate Secured Overnight Financing Rate (SOFR), explain its connection to the futures price, and show its potential to provide new insight into the argument on interest rate term structure. Based on the future price discovery theory, this study set up a hypothesis that short-term interest rates should contribute to forecasting long-term ones. The causality between interest rates in different terms of, which are separately represented by bilateral repurchase rate and the market yield of long-term U.S. government bonds, is explored in this research using the VAR model, as well as the Granger causality test. The conclusion of this research proves the hypothesis and indicates a single-direction causality between interest rate of different terms. However, the research also has drawbacks such as ignoring non-linear relations and evading the change of government policy. These problems need to be solved in the following study.

The Flash Crash: The Impact of High-Frequency Trading on the Stability of Financial Market

The growing prevalence of High-Frequency Trading (HFT) in financial markets has sparked intense debate over its impact on market stability. This paper aims to research the adverse effects that come along with HFT's enhancement of liquidity and price discovery efficiency. It contrasts the hardware infrastructure and execution speeds of high-frequency automated trading against manual trading and analyzes how the liquidity bubbles provided by HFT contribute to market volatility. This paper then concludes with three main points. First, HFT can pose a threat to market stability. Second, specific HFT strategies may mislead other market participants and lead to price distortions. Lastly, HFT may exacerbate market participation inequality, posing a market fairness challenge. Based on these findings, this paper recommends that regulatory authorities and policymakers pay closer attention to the potential risks of HFT and implement measures to maintain market stability and fairness.

The Influence of Institutional Investors' Clustering Behavior on A-Share Stock

Institutional investors' "clustering" behavior can have significant implications for the stock market, especially concerning its stability and the efficiency of the price discovery mechanisms. Recognizing this, this research utilized regression models to rigorously analyze the effects of such clustering practices on the abnormal returns and volatility of stocks that are targeted by Equity-oriented funds. This research also implemented a T-test to differentiate the impacts of various variables in both bull and bear market scenarios. The findings underscore that within China's A-share market, equity-oriented fund tend to favor certain sectors, with a significant inclination towards the manufacturing and finance sectors. Furthermore, the clustering behavior exhibited by these investors has diverse effects on stock abnormal returns and volatility, contingent upon the time scales and prevailing market conditions. This discovery provides insights into institutional investment strategies and the overarching stability of the market. For individual investors, it's crucial to note that stocks with heavy institutional ownership might exhibit increased volatility, particularly in bear market conditions. Therefore, incorporating this understanding could be important in investment decision-making processes.

Is China’s hog futures market effective? Based on the perspective of price discovery and hedging functions

ABSTRACT Based on daily data of China’s hog futures and spot prices from January 2021 to October 2023, this paper uses VAR-GARCH-BEKK to test the effectiveness of China’s hog futures market. The results of the study indicate that there is a two-way positive leading relationship between the hog futures market and the spot market. There is a significant two-way volatility spillover effect between hog futures market and hog spot market, but the hedging performance of hog futures is poor. Therefore, the effectiveness of China’s hog futures market needs to be further improved.

The “educational capital” of corporate boards and initial public offering pricing: Evidence from the US initial public offerings

AbstractWe unravel the conditions under which the type of education found among board members mitigates the uncertainty in new equity markets. Our results indicate that issuers in low R&D industries—as well as those faced with complex organizational structures—leave less money on the table when their boards are dominated by executives with managerial skills. Conversely, boards with highly specialized members in small and knowledge‐intensive firms reduce underpricing. Finally, we document that both types of board education lessen initial public offering share–price volatility in the immediate post‐issue period. This research is the first to derive conclusive evidence that more executives in the boards with managerial skills and boards with highly specialized members facilitate price discovery in signaling issuer reputation on the first trading day. This study has implications that we provide investors with unique non‐financial information, boards' “education capital,” that can be considered before making investment decisions.

Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market

AbstractPrice discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.

Contagion effects of permissionless, worthless cryptocurrency tokens: Evidence from the collapse of FTX

This paper investigates the price discovery relationships between FTT Token, issued by the cryptocurrency exchange FTX, and a set of assets and liabilities held by FTX amid a period of catastrophic financial decline by applying novel information flow measurement techniques. Results indicate that during key phases associated with the collapse of FTX, FTT Token had an informational lead over multiple assets, including cryptocurrencies such as Ethereum. Furthermore, we identify significant interactions between the FTT Token and both Robinhood shares and the token Serum, raising concerns about the direct influence of permissionless, technically valueless tokens on other assets and the potential challenges to market stability and investor protection. Our findings underscore the need for stronger policy-making, regulatory, and ethical considerations in cryptocurrency markets.

Understanding the impacts of dark pools on price discovery

I study how crossing networks, a type of dark pool, affect price discovery and market liquidity in the presence of noisy and heterogeneous trader signals. I identify a buffer function of crossing networks that helps mitigate traders’ losses from false signals. Additionally, I uncover an amplification effect. That is, when signal precision is high, crossing networks enhance price discovery. By contrast, when signal precision is low, crossing networks impair price discovery. These insights reconcile conflicting empirical evidence, yielding novel predictions and regulatory recommendations for equity and emerging markets.

Price discovery in Chinese PVC futures and spot markets: Impacts of COVID-19 and benchmark analysis

Despite being a minor futures category, Polyvinyl chloride (PVC) futures have emerging as a vital element in energy and chemical futures domain. Employing three benchmark models component share (CS), information share (IS), and information leadership share (ILS), this study explores the price discovery function of Chinese PVC futures and spot markets. It assesses whether PVC futures have matured into an effective hedging tool and reference point for spot markets, and also examines the impact of the COVID-19 pandemic on this price discovery relationship. Empirical analysis reveals that the futures market has become the primary site for price discovery in the Chinese PVC market. All the models consistently demonstrate a mature price discovery function in PVC futures, providing risk mitigation tools for industry players. However, post-pandemic dynamics indicate that price discovery in PVC markets primarily occurs within the spot market. This suggests that compared to the futures market, the PVC spot market is able to respond more quickly to the strong signals of industrial recovery after the end of the pandemic. The feedback and pricing efficiency of the PVC futures market in response to new market information are also influenced. Furthermore, our study offers better anticipation of future market prices.

Price Discovery and Volatility Spillover: An Empirical Analysis of Indian Futures-Spot Cardamom Markets

Price discovery and its related volatility spillover effect are significant indicators in commodity derivatives market to hedge the risk against sharp price fluctuations. This study empirically analyses the price discovery and volatility spillover using vector error correction model, Granger causality and bivariate exponential GARCH model (EGARCH) in Indian cardamom spot-futures markets. Daily time series data of spot market and near month futures contracts spanning the period from January 2009 to December 2020 have been used for the study. These price series data are based on the authenticated sources of Multi Commodity Exchange. The results of vector error correction model supported by Granger causality test indicates that spot market plays the role of leader as it is more efficient in reflecting the new information to prices. The results of bivariate EGARCH (1,1) model show that volatility spillover from cardamom spot market to futures market is dominant.

Price Discovery in High-Frequency Equity Markets: Evidence from Retail and Institutional Trades

Price Discovery in High-Frequency Equity Markets: Evidence from Retail and Institutional Trades

Friend or Foe of Efficiency? The Impact of Algorithmic Trading on Price Discovery in Indian Base Metal Futures

Friend or Foe of Efficiency? The Impact of Algorithmic Trading on Price Discovery in Indian Base Metal Futures

Price discovery and volatility connectedness in Indian gold market: a study of ETFs, spot and futures

Price discovery and volatility connectedness in Indian gold market: a study of ETFs, spot and futures

Price discovery and volatility connectedness in Indian gold market: a study of ETFs, spot and futures

Price discovery and volatility connectedness in Indian gold market: a study of ETFs, spot and futures

10-K Complexity, Analysts’ Forecasts, and Price Discovery in Capital Markets

10-K Complexity, Analysts’ Forecasts, and Price Discovery in Capital Markets

Improving Price Leadership Share for Measuring Price Discovery

Improving Price Leadership Share for Measuring Price Discovery

Media Recommendation and Market Risk: Evidence from Chinese Market

This study analyzes 23,292 TikTok short-video stock recommendations from 2022 to explore their impact on stock market performance using the Difference-In-Differences Model. Key findings include: (1) short video recommendations provide valuable information, significantly boosting stock price discovery; (2) using a large language model (ChatGPT), it was found that the impact of clustered recommendations on stock price discovery is enhanced when considering recommendation strength. This pioneering research offers insights into the relationship between short-video stock recommendations and abnormal stock returns, benefiting researchers, investors, corporations, and social media analysts.

The Evolution and Profitability of China's Futures Markets: A Comprehensive Review

Since its inception in the late 1980s, China's futures market has evolved into a pivotal component of the global financial landscape, serving purposes of price discovery, risk hedging, and trading opportunities across diverse commodities and financial instruments. By the end of 2023, the market boasted 131 listed futures and options products, underlining its growth and international influence. This comprehensive review explores the evolution and profitability of China's futures markets, encompassing historical developments, regulatory frameworks, and empirical insights. Key drivers of market expansion include regulatory reforms, technological advancements, and the market's resilience during crises, such as the COVID-19 pandemic. The study underscores the market's significant role in global commodities trading, influencing economic policies, and integrating advanced trading technologies. Understanding these dynamics is crucial for policymakers, market participants, and researchers navigating the complexities and opportunities within China's dynamic futures market landscape. This review positions China's futures market as a cornerstone of global financial markets, shaping economic development and market stability in an interconnected world economy.