There is so much assumed knowledge in the investing literature and posts online that sometimes it can be confusing. Users can treat this page as a dictionary-style reference page to refer back to at any point throughout the user journey on this topic.

This post could also be kept open in a separate page to refer back to easily while you are making your way through all the other articles in this topic.

If there are any other items that users want added and explained, you can contact us here and we will be happy to update the page.

• market: This term will be used everywhere on this site and elsewhere in social media etc. The market has a broad definition and will depend on the end-user but you can think of it as the main stock index of that country e.g a US report states "the market was up 2% today" they most likely mean the S+P 500, which most people treat as the main US index, was up 2%. They could also mean that the average of the S+P 500, the Nasdaq and the Dow Jones was up 2%. If we switched the example to the UK, it is most likely the FTSE 100 the market was referring to or the EuroStoxx 50 for Europe.
• alpha: refers to the ability to beat the market. Alpha measures the predictive power of a strategy or signal. It represents what returns the strategy is delivering to the investor, after accounting for the overall market performance - higher alpha means higher returns that are not explained by the market. If a manager or strategy is delivering no alpha then the strategy or manager is mostly noise.
• beta: the performance of the market is referred to as beta and is essentially what any investor can achieve by simply investing in the overall market. Another use case for beta occurs when a strategy is referred to as having no alpha, just beta. This means the strategy is delivering nothing in excess of what can be achieved by investing in the market directly.
• market risk premium: this is the return of the market minus the risk-free rate i.e the premium an investor obtains by investing in the market instead of leaving money in a bank account
• systematic risk: the risk of the market in general that investors need to be compensated for in the form of a risk premium. This is also known as non-diversifiable risk. With any investment there is risk and there should be some expected return associated to that risk. This return should therefore be higher than the risk-free rate, which is the rate an investor would receive for deposited cash in the bank and earn risk-free interest.
• unsystematic risk: this is also called non-systematic risk, idiosyncratic risk, specific risk or diversifiable risk. This is the portion of risk that can be removed from a portfolio by ensuring that enough stocks are chosen to eliminate the unsystematic risk.
• risk metric: no fixed definition but it generally refers to a metric that is risk-based that a person relies on to make decisions. This risk metric is most likely a volatility-based metric where the user is concerned about how many ups and downs something has compared to the average price. The higher the volatility the higher the risk.
• factor model: a framework by which you represent every single stock return by a strict model equation. Any coefficients in this model are most likely obtained from historical market data. The model's aim is to provide a clear representation to compare stocks against each other and to represent their returns via a simplified equation. We give an example in the capm model below.
• capm model: the capital asset pricing model. It was the first factor model in academia where the returns of a stock are represented by a beta value (also called a coefficient) multiplied by the market return. It is an articulate way to represent a stocks excess return

• passive investing: this form of investing uses low-cost and mostly standardised products that aim to match the market, such as trackers or mutual funds. They can be limited in scope due to the indices they track and inflexible.
• active investing: refers to the management of a portfolio mostly by a skilled investment/portfolio manager. The investment choices made are mostly hands-on and do not involve quantitative strategies. Their aim is to deliver superior returns to the market. However the fees are very expensive for such active management.
• smart beta: tries to combine or mix the passive and active styles to create the best of both worlds, a slightly quantitative approach that aims to deliver better returns than the market.

• annual volatility: this is a measure for the variation of data around its average value over a one-year horizon. It explains to the user how volatile an assets price is and the higher the volatility the riskier the asset.
• rebalancing: the process of ensuring that your portfolio is kept up to date with the strategy i.e stocks in your portfolio that are not in the strategy are sold and stocks in the strategy that are not in your portfolio are bought. Please refer to our rebalancing article for more detail.
• backtest: a method of historically testing how an investment strategy would have performed if you have invested in it back in time. It uses the investment to historically construct the portfolio you would have had using realistic execution prices to give you the set of portfolio returns you could have achieved. Please refer to our backtest article for more detail.
• sharpe ratio: similar to the concept of a risk-adjusted return. The Sharpe ratio is calculated by first subtracting the risk-free rate from the average return of the strategy and dividing this by the volatility of the strategy returns. Essentially this scales any returns an investor gets by the volatility needed to generate the returns. As a heuristic, any sharpe ratio > 1 is a decent investment strategy.
• rolling sharpe ratio: rolling refers to continually moving your historical window used for your calculations. This is different to an expanding window that some people are familiar with. An expanding window takes all data available for use in calculating statistics such as risk, return, sharpe ratio and so an expanding window grows with time. A rolling window in comparison ensures that only a fixed amount of data is used in any calculations of risk, return, sharpe ratio and when new data becomes available, that new data is included and other older historical data is removed.
• drawdown: a period of sustained negative performance is referred to as a drawdown. It represents in percentage terms how far your portfolio is from its previous highest value. Typically if your portfolio is not within 10-15% of its highest portfolio value, you are most likely classified as in a drawdown. However, serious drawdowns only occur when portfolios are around 20-25% or more below previous highs. Please refer to our drawdown article for more detail.
• drawdown length: the length of a drawdown is a useful metric to consider when looking at a strategy. What investors do not want to see is a very large drawdown length, where this could indicate that the strategy in question does not have the alpha or predictive power to bounce back and breach the historical peak quickly and investors in that strategy suffer for a very long time. Short, sharp drawdowns can and do occur but if your drawdown length is short then investors are more likely to believe in the bounce-back nature of the strategy for the future. They believe it has enough alpha to perform again after a negative period.
• tear-sheets: industry term for a set of backtested results that indicate the efficacy of the strategy being analysed. It usually contains a strategy equity performance graph, risk and return and sharpe ratio statistics and a set of yearly returns so users can see how the strategy performed on a year-by-year basis. Please refer to our tear-sheet article for more detail.
• Training data: this is the data used when your model is fitted and coefficients are estimated. Potentially the model is tweaked and changed multiple times during this training phase and so has the potential to overfit to this training data if care is not taken.
• Test data: this is data that your model does not see during its training. Once your model is fitted using the training data, it is then only ran against the test data. A nice property for your model to have is for the error to be the same when ran on the training data and the test data. This ensures that your model could potentially generalise well and produce good results when run in a live performing environment.

• transaction costs: bid-offer spreads mean you most often do not get the mid price
• market impact: this affects larger institutional clients more than smaller retail investors and also depends on the type of stock that is being bought. Market impact refers to the impact your own trades have on the price of the underlying stock during the execution phase of your order.
• Slippage: the fill price you received differs from what you assumed or expected you would get. For quantitative modelling, this can be important as backtested results assume you are filled with a price that you may not have achieved in real life. Slippage for example can be driven by:
  • the order size being too large so the price move during execution
  • time delay placing your order to the broker so the market moved
  • stop-loss fill price differs from stop-loss order entered
• adv: the average daily volume. This is the total US$ amount of liquidity that a stock trades on a given day. It is calculated by summing up all the trades multiplied by their transaction prices. This gives us a final total amount of dollars that were transacted for that stock.
• vwap: the volume-weighted average price is a price that is based on a weighted average summation of all order sizes and prices for that security over a specified period.
• pov: percentage-of-volume is another metric used by larger investment firms where there orders are executed via brokers
• twap: time-weighted average price that represents a similar concept to the vwap price. Twap refers to time-weighting so the execution algorithm fills your orders at a specific and consistent speed, it represents the security's average price. This is a lesser used fill method than both vwap and pov.
• limit order: is an order to buy or sell an asset at a certain price. This order is not guaranteed to get filled.
• market order: is an order to buy or sell an asset immediately by executing at the best price available. Whilst it gives certainty to execution the slippage with this type of order can be more than an investor desires and is especially important for either large size market orders or orders executed for illiquid stocks.
• stop-loss order: an order placed to ensure that an investor limits the losses on positions. It is an order to exit a losing position using a pre-defined price if the market drops to this price. Again, slippage can be a concern here because if the market is moving quickly the executing broker will use best efforts to fill the stop-loss order but may end up executing at a worse price.
• pegged-to-market: this is an order and execution methodology where the order is placed on either the best bid price (if trying to buy) or best offer price (if trying to sell). If the price moves and the order is not filled, then the order updates with the price and again joins either the best bid or best offer side of the market. This simple form of a peg order can ensure that the investor does not cross the bid-offer spread and could end up getting filled at better rates than a market order. However the inverse is also true and by being passive with the execution, an investor may not get filled and instead have to purchase for example at a much higher price later in the day in order to execute.
• opening and closing auctions: the stock market provides two extra methods of filling orders, that have gained in popularity significantly over recent years. These are auction style events where client orders are entered alongside each other to come up with a composite price. Closing auctions in particular (are far more prevalent than opening auctions) now account for a significanly higher percentage of investors fills (upwards of 15% of the day's volume) as they provide a reliable and transparent method of achieving the closing price for the investors fills.

• Corporate actions: this is an umbrella term that refers to company actions that change the corporate structure in terms of shares outstanding and price. These changes do not have any financial impact and there is no change to the market cap of the company. See stock splot and reverse split next for further explanation.
• Stock split: a company increases the number of shares it has issued, with an accompanying pro-rate decrease in the stock price so the market cap remains unchanged. This is primarily executed by companies who share prices have grown too large so a single share costs too much, especially for retail focused investors. By splitting their stock they ensure that the price decrease allows more users to buy shares. An example is a 10-for-1 split where the number of shares increases by 10-fold and the share price then effectively drops by 90%, no losses are incurred by current shareholders as they get 10 shares for every 1 share they previously owned.
• Reverse split: this is the opposite of a stock split. In a reverse split, a company decreases its outstanding shares and thus aritifically increases its share price. It may do this because it wants to increase it's stock price, maybe due to potential delisting risk. Another reason for a reverse split may be to increase the chances of the stocks inclusion on an exchange or participate in an index or etf.
• Spin-offs: When a company takes a business unit and spins it out as a separate business with its own revenue stream.
• Survivorship bias: only including companies that are alive today in your universe back through time and ignoring companies that delisted or went bankrupt. A quantitative investment companies data stack and backtester should be free of survivorship bias.
• Lookahead bias: another form of data bias where we allow our backtester, or strategy builder, to use data at a point in time that was only known in the future. This means all datasets in a companies data pipeline have to ensure that the data used was only data that was time-stamped and known from a particular day onwards, and not before.
• Point-in-time (PIT): links closely with lookahead bias and means that we only use data at a point in time when the market and the participants would have received that data.

If there are any other items that users want added and explained, please contact us here and we will be happy to update the page.