Economic factors

Economic factors

These include: (a)economic policy, disseminated by government agencies and central banks, (b)economic conditions, generally revealed through economic reports, and other economic indicators.
Economic policy comprises government fiscal policy (budget/spending practices) and monetary policy (the means by which a government's central bank influences the supply and "cost" of money, which is reflected by the level of interest rates).
Government budget deficits or surpluses: The market usually reacts negatively to widening government budget deficits, and positively to narrowing budget deficits. The impact is reflected in the value of a country's
currency.

Balance of trade levels and trends: The trade flow between countries illustrates the demand for goods and services, which in turn indicates demand for a country's currency to conduct trade. Surpluses and deficits in trade of goods and services reflect the competitiveness of a nation's economy. For example, trade deficits may have a negative impact on a nation's currency.

Inflation levels and trends: Typically a currency will lose value if there is a high level of inflation in the country or if inflation levels are perceived to be rising. This is because inflation erodes purchasing power, thus demand, for that particular currency. However, a currency may sometimes strengthen when inflation rises because of expectations that the central bank will raise short-term interest rates to combat rising inflation.
Economic growth and health: Reports such as GDP, employment levels, retail sales, capacity utilization and others, detail the levels of a country's economic growth and health. Generally, the more healthy and robust a country's economy, the better its currency will perform, and the more demand for it there will be.
Productivity of an economy: Increasing productivity in an economy should positively influence the value of its currency. Its effects are more prominent if the increase is in the traded sector

High-frequency trading

High-frequency trading (HFT) is the use of sophisticated technological tools to trade securities like stocks or options, and is typically characterized by several distinguishing features
HFT is highly quantitative, employing computerized algorithms to analyze incoming market data and implement proprietary trading strategies;

HFT usually implies a firm holds an investment position only for very brief periods of time - even just seconds - and rapidly trades into and out of those positions, sometimes thousands or tens of thousands of times a day;
HFT firms typically end a trading day with no net investment position in the securities they trade;
HFT operations are usually found in proprietary firms or on proprietary trading desks in larger, diversified firms;
HFT strategies are usually very sensitive to the processing speed of markets and of their own access to the market.

In high-frequency trading, programs analyze market data to capture trading opportunities that may open up for only a fraction of a second to several hours.High-frequency trading (HFT) uses computer programs and sometimes specialised hardware  to hold short-term positions in equities, options, futures, ETFs, currencies, and other financial instruments that possess electronic trading capability.[4] High-frequency traders compete on a basis of speed with other high-frequency traders, not long-term investors (who typically look for opportunities over a period of weeks, months, or years), and compete with each other for very small, consistent profits. As a result, high-frequency trading has been shown to have a potential Sharpe ratio (measure of reward per unit of risk) thousands of times higher than the traditional buy-and-hold strategies. By 2010 high-frequency trading accounted for over 70% of equity trades taking place in the US and was rapidly growing in popularity in Europe and Asia. Aiming to capture just a fraction of a penny per share or currency unit on every trade, high-frequency traders move in and out of such short-term positions several times each day. Fractions of a penny accumulate fast to produce significantly positive results at the end of every day. High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.

One financial industry source claims algorithmic trading, including high-frequency trading, substantially improves market liquidity.An academic study shows additional benefits, including lowering the costs of trading,increasing the informativeness of quotes,improved linkage between markets, and other positive spillover effects, at least in quiescent or stable markets; the authors of this study also note that "it remains an open question whether algorithmic trading and algorithmic liquidity supply are equally beneficial in more turbulent or declining markets...algorithmic liquidity suppliers may simply turn off their machines when markets spike downward."Also noteworthy is that HFT only takes place in markets that are already deemed liquid, hence calling its necessity into question.

Algorithmic and high-frequency trading were both implicated in the May 6, 2010 Flash Crash, when high-frequency liquidity providers were in fact found to have withdrawn from the market. A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."

Mean Reversion

Mean Reversion

Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock's high and low prices are temporary, and that a stock's price will tend to have an average price over time.

Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc.

When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.

The Standard deviation of the most recent prices (e.g., the last 20) is often used as a buy or sell indicator.

Stock reporting services (such as Yahoo! Finance, MS Investor, Morningstar, etc.), commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.

Mean reversion has the appearance of a more scientific method of choosing stock buy and sell points than charting, because precise numerical values are derived from historical data to identify the buy/sell values, rather than trying to interpret price movements using charts (charting, also known as technical analysis).