Kissell Research Group Data Sets

The following descriptions define the data sets provided in the file KRGExampleData.mat.

`Basket` Variables

The table Basket contains a trade list for a collection of stocks in a portfolio. For examples of using this data set, see Rank Broker Performance.

Real trade lists come from portfolio managers.

Table Variable	Description
`Symbols`	Stock symbol.
`Side`	Side (`'B'` or `'S'`).
`Size`	Size (number of shares divided by average daily volume).
`Shares`	Number of shares.
`Price`	Stock price.
`ADV`	Average daily volume.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`POV`	Percentage of volume.

`BrokerNames` Variables

The table BrokerNames contains the broker names and their associated market-impact code. For examples of using this data set, see Rank Broker Performance.

Real trade lists come from portfolio managers.

Table Variable	Description
`Broker`	Broker name.
`MICode`	Market-impact code (`1`, `2`, `3`, and so on).

`TradeData` Variables

The table TradeData provides example data for a collection of stocks in a transaction. For examples of using this data set, see Conduct Sensitivity Analysis to Estimate Trading Costs and Estimate Portfolio Liquidation Costs.

Real market data comes from a data source such as Bloomberg^®.

Table Variable	Description
`Symbol`	Stock symbol.
`Side`	Side (`'Buy'` or `'Sell'`).
`SideIndicator`	Side indicator. `1` is a buy (add shares to portfolio). `-1` is a sell (remove shares from portfolio).
`AvgExecPrice`	Average execution price.
`ArrivalPrice`	Arrival price. The price at the time the order enters the market.
`PeriodVWAP`	Volume weighted average price (VWAP). The VWAP compares the execution price to the interval VWAP price.
`CCYRate`	Currency rate.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`POV`	Percentage of volume.
`SectorCategory`	Market sector category (`'Energy'`, `'Industrials'`, `'Materials'`, and so on).
`OrderSizeCategory`	Order size category (`'Large'`, `'Medium'`, or `'Small'`).
`VolatilityCategory`	Volatility category (`'High'`, `'Medium'`, or `'Low'`).
`POVRateCategory`	Percentage of volume rate category (`'Aggressive'`, `'Passive'`, or `'Normal'`).
`MktCapCategory`	Market capitalization category (`'LC'` is large cap, `'MC'` is mid cap, `'SM'` is small cap).
`MomentumCategory`	Momentum category (`'Favorable'`, `'Neutral'`, or `'Adverse'`).
`MktMovementCategory`	Market movement category (`'Favorable'`, `'Neutral'`, or `'Adverse'`).
`ADV`	Average daily volume.
`Price`	Stock price.
`Size`	Size (number of shares divided by average daily volume).
`Alpha_bp`	Alpha estimate per day in basis points.
`Shares`	Number of shares.
`Broker`	Broker name.

`TradeDataCurrent` and `TradeDataHistorical` Variables

The tables TradeDataCurrent and TradeDataHistorical provide example current and historical data, respectively, for a collection of stocks in a transaction. For an example of using this data set, see Determine Buy-Sell Imbalance Using Cost Index.

Real market data comes from a data source such as Bloomberg.

Table Variable	Description
`Symbol`	Stock symbol.
`Date`	Transaction date.
`MICode`	Market-impact code (`1`, `2`, `3`, and so on).
`Open`	Stock open price.
`VWAP`	Volume weighted average price (VWAP).
`Last`	Stock last price.
`Volume`	Trade volume.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`ADV`	Average daily volume.
`Beta`	Beta.
`IndexOpen`	Index open price.
`IndexVWAP`	Index VWAP.
`IndexLast`	Index last price.
`Price`	Stock price.
`POV`	Percentage of volume.
`Shares`	Number of shares.

`PortfolioData` Variables

The table PortfolioData provides example data for a collection of stocks in a portfolio. To use this data set, see portfolioCostCurves.

Real portfolio data comes from a portfolio belonging to a company or portfolio manager.

Table Variable	Description
`Symbol`	Stock symbol.
`Price_Local`	Local price of the stock.
`Price_Currency`	Stock price with a specified base currency if the stock trades outside the United States. If the stock trades in the United States, `Price_Currency` has the same value as `Price_Local`.
`ADV`	Average daily volume.
`Volatility`	Volatility.
`Shares`	Number of shares.

`PostTradeData` Variables

The table PostTradeData provides example data for a collection of stocks in an executed transaction. To use this data set, see Analyze Trading Execution Results.

Real market data comes from a data source such as Bloomberg.

Table Variable	Description
`Symbol`	Stock symbol.
`Side`	Side (`'Buy'` or `'Sell'`).
`SideIndicator`	Side indicator. `1` is a buy (add shares to portfolio). `-1` is a sell (remove shares from portfolio).
`Date`	Transaction date.
`DecisionTime`	Decision time. The portfolio manager decides to buy, sell, short, or cover a position at this time. If no other timestamp is available, set this variable to the time when the portfolio manager enters the order into the trading system. If the portfolio manager does not have a timestamp for this decision, investors use the close time of the previous day, open time, or arrival time.
`ArrivalTime`	Arrival time. The trading system enters the order into the market for execution at this time. You can obtain it from the first trade from the electronic audit trail.
`EndTime`	End time. The portfolio manager specifies to complete the order at this time. Typically, this time is the end of the day or the time of the last trade.
`AvgExecPrice`	Average executed price.
`OrderShares`	Number of shares.
`TradedShares`	Number of shares executed.
`Volatility`	Volatility.
`ADV`	Average daily volume.
`POV`	Percentage of volume.
`CCYRate`	Currency rate.
`MICategory`	Market-impact category (for example, `1`).
`PrevClose`	Close price of the previous day.
`Open`	Open price.
`Close`	Close price.
`ArrivalPrice`	Arrival price. The price at the time the order enters the market.
`PeriodVWAP`	Volume weighted average price (VWAP). The VWAP compares the execution price to the interval VWAP price.
`Broker`	Broker name.
`Algorithm`	Trading algorithm (`'Dark Pool'`, `'TWAP'`, `'Arrival'`, and so on).
`Manager`	Portfolio manager name.
`Trader`	Trader name.
`SectorCategory`	Market sector category (`'Energy'`, `'Industrials'`, `'Materials'`, and so on).
`OrderSizeCategory`	Order size category (`'Large'`, `'Medium'`, or `'Small'`).
`VolatilityCategory`	Volatility category (`'High'`, `'Medium'`, or `'Low'`).
`POVRateCategory`	Percentage of volume rate category (`'Aggressive'`, `'Passive'`, or `'Normal'`).
`MktCapCategory`	Market capitalization category (`'LC'` is large cap, `'MC'` is mid cap, `'SM'` is small cap).
`StockMomentumCategory`	Stock momentum category (`'Favorable'`, `'Neutral'`, or `'Adverse'`).
`MktMovementCategory`	Market movement category (`'Favorable'`, `'Neutral'`, or `'Adverse'`).
`StepOut`	Investor field designation. This variable is optional for grouping and summary analysis. This field refers to a process where a broker (broker 1) receives an order from a client. Then this broker gives that order to another broker (broker 2) for its execution. Broker 1 receives credit for the trade but its performance applies to broker 2 who executed the trade.
`ISDecisionPrice`	Decision price. This variable is the stock price when the portfolio manager decides to buy, sell, short, or cover a position.
`ISArrivalPrice`	Midpoint of the bid-ask spread at the time an order enters the market.
`ISEndPrice`	End price. This variable is the stock price at the specified end time of the order.
`ISFixedDollars`	Fixed fees in dollars that include the commission, taxes, clearing and settlement charges, and so on.

`TradeDataBackTest` Variables

The table TradeDataBackTest provides example data for a set of stocks and a series of dates. The data contains historical trade information for each stock. To use this data set, see Conduct Back Test on Portfolio.

Real market data comes from a data source such as Bloomberg.

Table Variable	Description
`Symbol`	Stock symbol.
`Date`	Historical transaction date.
`Shares`	Number of shares.
`Side`	Side (`'Buy'` or `'Sell'`).
`Value`	Dollar value of the stock in the portfolio.
`Price`	Stock price.
`Size`	Size (number of shares divided by average daily volume).
`EstReturn`	Estimated return decimal value for the stock in the portfolio.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`ADV`	Average daily volume.
`MktCap`	Market capitalization.
`TradeTime`	Trade duration time.
`POVRate`	Percentage of volume rate.
`MICode`	Market-impact code (`1`, `2`, `3`, and so on).
`FXRate`	Foreign exchange rate.
`POV`	Percentage of volume.

`TradeDataStressTest` Variables

The table TradeDataStressTest provides example data for a set of stocks for a date range. The data contains trade information for each stock. To use this data set, see Conduct Stress Test on Portfolio.

Real market data comes from a data source such as Bloomberg.

Table Variable	Description
`Symbol`	Stock symbol.
`Date`	Historical transaction date.
`Shares`	Number of shares.
`Side`	Side (`'Buy'` or `'Sell'`).
`Value`	Dollar value of the stock in the portfolio.
`Price`	Stock price.
`Size`	Size (number of shares divided by average daily volume).
`EstReturn`	Estimated return decimal value for the stock in the portfolio.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`ADV`	Average daily volume.
`MktCap`	Market capitalization.
`TradeTime`	Trade duration time.
`POVRate`	Percentage of volume rate.
`MICode`	Market-impact code (`1`, `2`, `3`, and so on).
`FXRate`	Foreign exchange rate.

`TradeDataPortOpt` Variables

The table TradeDataPortOpt contains example data for a collection of stocks in a portfolio. This data contains lower and upper bounds for the constraints used in the portfolio optimization. To use this data set, see Liquidate Dollar Value from Portfolio.

To see the related covariance data for each stock in the portfolio, see the covariance data table CovarianceData.

Real portfolio data comes from a portfolio belonging to a company or portfolio manager.

Table Variable	Description
`Symbol`	Stock symbol.
`Date`	Transaction date.
`Shares`	Number of shares.
`Value`	Dollar value of the stock in the portfolio.
`Price`	Stock price.
`Size`	Size (number of shares divided by average daily volume).
`EstReturn`	Estimated return decimal value for the stock in the portfolio.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`ADV`	Average daily volume.
`MktCap`	Market capitalization.
`TradeTime`	Trade time.
`MICode`	Market-impact code (`1`, `2`, `3`, and so on).
`LB_Wt`	Lower bound weight.
`UB_Wt`	Upper bound weight.
`LB_MinShares`	Lower bound for the minimum shares.
`UB_MaxShares`	Upper bound for the maximum shares.
`LB_MinPctADV`	Lower bound for the minimum percentage of average daily volume.
`UB_MaxPctADV`	Upper bound for the maximum percentage of average daily volume.
`LB_MinValue`	Lower bound for the minimum value.
`UB_MaxValue`	Upper bound for the maximum value.
`UB_MaxMI`	Upper bound for the maximum market-impact cost.

`TradeDataTradeOpt` Variables

The table TradeDataTradeOpt provides an example trade list for a collection of stocks in a portfolio. For an example of using this data set, see Optimize Trade Schedule Trading Strategy for Basket.

Real trade lists come from portfolio managers.

Table Variable	Description
`Date`	Transaction date.
`Side`	Side (`'B'` or `'S'`).
`Shares`	Number of shares.
`Price`	Stock price.
`ADV`	Average daily volume.
`Volatility`	A statistical measure of the dispersion of daily returns for a given security. Volatility is the standard deviation of daily log price returns over time. Kissell Research Group uses a 30-day historical period. Annualize volatility by multiplying by the square root of 250.
`PctADV`	Percentage of average daily volume.
`Value`	Transaction value.
`Weight`	Weight.
`SideIndicator`	Side indicator. `1` is a buy (add shares to portfolio). `-1` is a sell (remove shares from portfolio).
`MIRegion`	Market-impact region.
`Symbol`	Stock symbol.
`Alpha_bp`	Alpha in basis points.
`Beta`	Beta.
`Sector`	Market sector, such as Energy.
`MktCap`	Market capitalization.

`CovarianceData` Table

The table CovarianceData contains a covariance value for all stocks in the portfolio data table TradeDataPortOpt. Each variable in the table is a different stock. To use this data set in the portfolio optimization, see Liquidate Dollar Value from Portfolio.

`CovarianceTradeOpt` Table

The table CovarianceTradeOpt contains a covariance value for each stock in the portfolio data table TradeDataTradeOpt. Each variable in the table is a different stock. To use this data set in the trade schedule optimization, see Optimize Trade Schedule Trading Strategy for Basket.

References

[1] Kissell, Robert. “A Practical Framework for Transaction Cost Analysis.” Journal of Trading. Vol. 3, Number 2, Summer 2008, pp. 29–37.

[2] Kissell, Robert. “The Expanded Implementation Shortfall: Understanding Transaction Cost Components.” Journal of Trading. Vol. 1, Number 3, Summer 2006, pp. 6–16.

[3] Kissell, Robert. The Science of Algorithmic Trading and Portfolio Management. Cambridge, MA: Elsevier/Academic Press, 2013.

[4] Kissell, Robert, and Morton Glantz. Optimal Trading Strategies. New York, NY: AMACOM, Inc., 2003.