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Dynamics of Noise Traders’ Risk in the NSE and BSE Markets


Affiliations
1 Rabindra Mahavidyalaya (Affiliated to The University of Burdwan, Burdwan, W.B.), West Bengal, India
     

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In Financial Economics, the "noise" and "noise traders" play critical roles in stocks' equilibrium pricing mechanism. It includes economic and non-economic aspects. The paper empirically explores the nature and magnitude of noise traders' risk in India during the present recovery phase. Besides the daily trading data, it utilizes intra-day 1D and 5D trade-prices, trade-volumes, and trade-times of the Nifty-Fifty firms listed both in the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). The study utilizes the NSE-Nifty and the BSE-Sensex indices for market return data. It examines whether stocks' return variations incorporate noise traders' risk or not and whether informed traders' short-run arbitrage forces them to long-short positioning for hedging or not.

The study argues that noise has systematic and firm-specific components those vary over time. These components include idiosyncratic and noise aspects. At lag-periods, traders' longshort positions over these markets can hedge fundamental systematic and fundamental firm-specific shocks and may detach noise shocks. Once stocks are traded at long - short horizons, traders' long-short returns expose the noise aspects across stocks The study also compares the results for the current price-volume-trade time data with those of two years earlier. The findings suggest that intra-day returns from 1D and 5D data impound significant noise while daily (weekly) returns show its high (moderate) exposures. The conditional volatilities of long-short returns in the GARCH models show that the time-varying idiosyncratic noise is highly persistent at presence of noise traders. The study confirms that stocks' prices impound information and noise during the trading days.


Keywords

Pricing Equilibrium, Economic Recovery, Noise Trading, Systematic Noise, Idiosyncratic Noise, GARCH Models.
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  • Dynamics of Noise Traders’ Risk in the NSE and BSE Markets

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Authors

Paritosh Chandra Sinha
Rabindra Mahavidyalaya (Affiliated to The University of Burdwan, Burdwan, W.B.), West Bengal, India

Abstract


In Financial Economics, the "noise" and "noise traders" play critical roles in stocks' equilibrium pricing mechanism. It includes economic and non-economic aspects. The paper empirically explores the nature and magnitude of noise traders' risk in India during the present recovery phase. Besides the daily trading data, it utilizes intra-day 1D and 5D trade-prices, trade-volumes, and trade-times of the Nifty-Fifty firms listed both in the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). The study utilizes the NSE-Nifty and the BSE-Sensex indices for market return data. It examines whether stocks' return variations incorporate noise traders' risk or not and whether informed traders' short-run arbitrage forces them to long-short positioning for hedging or not.

The study argues that noise has systematic and firm-specific components those vary over time. These components include idiosyncratic and noise aspects. At lag-periods, traders' longshort positions over these markets can hedge fundamental systematic and fundamental firm-specific shocks and may detach noise shocks. Once stocks are traded at long - short horizons, traders' long-short returns expose the noise aspects across stocks The study also compares the results for the current price-volume-trade time data with those of two years earlier. The findings suggest that intra-day returns from 1D and 5D data impound significant noise while daily (weekly) returns show its high (moderate) exposures. The conditional volatilities of long-short returns in the GARCH models show that the time-varying idiosyncratic noise is highly persistent at presence of noise traders. The study confirms that stocks' prices impound information and noise during the trading days.


Keywords


Pricing Equilibrium, Economic Recovery, Noise Trading, Systematic Noise, Idiosyncratic Noise, GARCH Models.