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Read Beta and the Market Model
अद्ययावत 22 ऑगस्ट 2026 · 3 मिनिटांचे वाचन · 14 अनुक्रमणिका
सोप्या भाषेतील अर्थ
The Market Model regresses one exact listing's excess daily returns on the configured benchmark's excess returns over a selected historical window. It reports beta, annualized alpha, R-squared, idiosyncratic volatility and total volatility as descriptions of that sample, never expected returns.
Why it is useful #
It separates market-linked movement from residual movement and publishes the benchmark, common-session count, risk-free-rate assumption, source and window. This helps users see when a familiar beta rests on limited overlap or a weak R-squared.
Where it appears and the workflow #
Open Beta and Market Model, select exchange and exact symbol, choose 60 to 1,250 sessions and an annual risk-free rate from 0% to 25%, then run. Confirm benchmark and common sessions before reading beta, alpha or volatility. Change one assumption at a time and keep the original window for comparison.
Calculation or source #
The DB-only service uses persisted adjusted listing closes and index closes on dates both traded, converts them to simple close-to-close returns, subtracts the daily risk-free component and fits a single-factor linear regression. Alpha is annualized by 252 and volatility by the square root of 252 sessions.
Prerequisites, inputs, period and unit #
Prerequisites are a configured exchange benchmark, positive stored closes for both series and enough overlapping return observations. Beta and R-squared are ratios; alpha and volatility are annualized percentages. The supplied risk-free rate is an assumption, not a live sourced rate, and the response publishes it.
Worked example #
Illustrative only: beta 1.20 with R-squared 0.55 means the fitted listing moved about 1.2% in excess-return terms for a 1% benchmark move and the model explained 55% of observed variance in that window. It does not predict the next move or imply a 20% premium.
What high and low mean #
Higher beta means greater fitted sensitivity, not better performance. Higher R-squared means the one-factor benchmark explained more of the sample variance. Higher idiosyncratic volatility means more movement remained unexplained by that benchmark; it is not a direction signal.
Positive, negative and zero #
Alpha can be positive, negative or genuinely zero after the stated model and annualization. Beta can be below or above zero. Missing metrics remain null when the regression is not valid; they must not be rendered as zero, neutral or an average market response.
Limitations and common mistakes #
Results depend on window, benchmark, adjustment basis, outliers and risk-free assumption. Alpha is not manager skill, R-squared is not accuracy and idiosyncratic volatility is not guaranteed diversifiability for a real portfolio. Common mistakes include comparing different benchmarks or treating annualized sample statistics as forecasts.
Market-specific differences #
Each exchange uses its published configured benchmark and local completed sessions. Listing and index holidays can reduce overlap. Native price currencies are preserved in source data, while return ratios are dimensionless; a cross-listed security on another venue requires a separate run.
Suggested next steps #
Record benchmark, window, sessions and risk-free assumption before reading the coefficients. Repeat with a shorter window and explain every change as sample sensitivity, then compare R-squared with residual volatility before writing a non-predictive conclusion.
Educational use only #
This guide explains descriptive research evidence. It is not investment advice, a price prediction, a recommendation, a claim of predictive accuracy, or an instruction to buy, sell, rebalance or place an order.