Trade BSE and NSE stocks by size, going long on the smallest quintile and short on the largest, using value-weighted portfolios rebalanced monthly based on market equity logarithms.
ASSET CLASS: stocks | REGION: Emerging Markets, India | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Size Effect, India
I. STRATEGY IN A NUTSHELL
The strategy trades BSE and NSE stocks sorted by market capitalization. It goes long the smallest quintile (small-cap stocks) and short the largest quintile (large-cap stocks). Portfolios are value-weighted and rebalanced monthly.
II. ECONOMIC RATIONALE
Small-cap stocks tend to outperform large-cap stocks, reflecting a size premium. This arises because smaller firms are generally riskier, more volatile, and have greater growth potential, leading to higher expected returns. The study confirms that this size effect holds in the Indian market.
III. SOURCE PAPER
Cross-sectional Return Predictability in Indian Stock Market: An Empirical Investigation [Click to Open PDF]
Goswami, Gautam, Fordham University – Finance Area
<Abstract>
This paper provides a comprehensive analysis of stock return predictability in the Indian stock market by employing both the portfolio and cross-sectional regressions methods using the data from January 1994 and ending in December 2018. We find strong predictive power of size, cash-flow-to-price ratio, momentum and short-term-reversal, and in some cases of book-to-market-ratio, price-earnings-ratio. The total volatility, idiosyncratic volatility, and beta are not consistent stock return predictors in the Indian stock market. In cross-sectional regression analysis, size, short-term reversal, momentum, and cash-flow-to-price ratio predict the future stock returns. Overall, the two variables momentum and cash flow to price ratio demonstrate reliable forecasting power under all methods and both small and large size samples.
IV. BACKTEST PERFORMANCE
| Annualised Return | 10.3% |
| Volatility | 25.64% |
| Beta | -0.025 |
| Sharpe Ratio | 0.4 |
| Sortino Ratio | N/A |
| Maximum Drawdown | N/A |
| Win Rate | 50% |
V. FULL PYTHON CODE
from AlgorithmImports import *
from typing import Dict, List
import data_tools
# endregion
class TheSizeEffectinIndianMarket(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000000) # INR
self.quantile:int = 5
self.leverage:int = 5
self.data:Dict[Symbol, data_tools.SymbolData] = {}
# download tickers
ticker_file_str:str = self.Download('data.quantpedia.com/backtesting_data/equity/india_stocks/nse_500_tickers.csv')
ticker_lines:List[str] = ticker_file_str.split('\r\n')
tickers:List[str] = [ ticker_line.split(',')[0] for ticker_line in ticker_lines[1:] ]
for t in tickers:
# price data subscription
data = self.AddData(data_tools.IndiaStocks, t, Resolution.Daily)
data.SetFeeModel(data_tools.CustomFeeModel())
data.SetLeverage(self.leverage)
stock_symbol:Symbol = data.Symbol
# fundamental data subscription
balance_sheet = self.AddData(data_tools.IndiaBalanceSheet, t, Resolution.Daily).Symbol
self.data[stock_symbol] = data_tools.SymbolData(stock_symbol, balance_sheet)
self.recent_month:int = -1
def OnData(self, data: Slice) -> None:
rebalance_flag:bool = False
metric_by_symbol:Dict[Symbol, float] = {}
price_last_update_date:Dict[Symbol, datetime.date] = data_tools.IndiaStocks.get_last_update_date()
bs_last_update_date:Dict[Symbol, datetime.date] = data_tools.IndiaBalanceSheet.get_last_update_date()
for price_symbol, symbol_data in self.data.items():
# store price data
if data.ContainsKey(price_symbol) and data[price_symbol] and data[price_symbol].Value != 0:
price: float = data[price_symbol].Value
self.data[price_symbol].update_price(price)
bs_symbol:Symbol = symbol_data._balance_sheet_symbol
# check if BS statement is present
if bs_symbol in data and data[bs_symbol]:
bs_statement:Dict = data[bs_symbol].Statement
shares_field:str = 'commonStockSharesOutstanding'
if shares_field in bs_statement and bs_statement[shares_field] is not None:
shares_outstanding:float = float(bs_statement[shares_field])
# store fundamentals
symbol_data.update_fundamentals(shares_outstanding)
if self.IsWarmingUp:
continue
# monthly rebalance
if self.Time.month != self.recent_month or rebalance_flag:
self.recent_month = self.Time.month
rebalance_flag = True
# fundamental data are ready and still arriving
if self.Securities[price_symbol].GetLastData() and price_symbol in price_last_update_date and self.Time.date() <= price_last_update_date[price_symbol]:
if self.Securities[bs_symbol].GetLastData() and bs_symbol in bs_last_update_date and self.Time.date() <= bs_last_update_date[bs_symbol]:
shares_outstanding:float = symbol_data.get_fundamentals()
price:float = symbol_data.get_price()
# calculate market capitalization
if price != -1 and shares_outstanding != -1:
metric_by_symbol[price_symbol] = shares_outstanding * price
if rebalance_flag:
weights:Dict[Symbol, float] = {}
if len(metric_by_symbol) >= self.quantile:
# sort by market capitalization
sorted_market_cap = sorted(metric_by_symbol, key=metric_by_symbol.get)
quantile: int = int(len(sorted_market_cap) / self.quantile)
# get top and bottom quintile
long_assets:List[Symbol] = sorted_market_cap[:quantile]
short_assets:List[Symbol] = sorted_market_cap[-quantile:]
# calculate weights based on values
sum_long = sum([metric_by_symbol[i] for i in long_assets])
for asset in long_assets:
weights[asset] = metric_by_symbol[asset] / sum_long
sum_short = sum([metric_by_symbol[i] for i in short_assets])
for asset in short_assets:
weights[asset] = -metric_by_symbol[asset] / sum_short
# liquidate and rebalance
invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
for price_symbol in invested:
if price_symbol not in weights:
self.Liquidate(price_symbol)
for price_symbol, weight in weights.items():
if price_symbol in data and data[price_symbol]:
self.SetHoldings(price_symbol, weight)