Quantitative analysis (finance)

Quantitative analysis is the use of mathematical and statistical methods in finance. Those working in the field are quantitative analysts. Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management; the occupation is similar to those in industrial mathematics in other industries. The process consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns; the resulting strategies may involve high-frequency trading. Although the original quantitative analysts were "sell side quants" from market maker firms, concerned with derivatives pricing and risk management, the meaning of the term has expanded over time to include those individuals involved in any application of mathematical finance, including the buy side. Examples include statistical arbitrage, quantitative investment management, algorithmic trading, electronic market making; some of the larger investment managers using quantitative analysis include Renaissance Technologies, Winton Group, D. E. Shaw & Co.

AQR Capital Management, Two Sigma Investments. Quantitative finance started in 1900 with Louis Bachelier's doctoral thesis "Theory of Speculation", which provided a model to price options under a normal distribution. Harry Markowitz's 1952 doctoral thesis "Portfolio Selection" and its published version was one of the first efforts in economics journals to formally adapt mathematical concepts to finance. Markowitz formalized a notion of mean return and covariances for common stocks which allowed him to quantify the concept of "diversification" in a market, he showed how to compute the mean return and variance for a given portfolio and argued that investors should hold only those portfolios whose variance is minimal among all portfolios with a given mean return. Although the language of finance now involves Itō calculus, management of risk in a quantifiable manner underlies much of the modern theory. In 1965 Paul Samuelson introduced stochastic calculus into the study of finance. N 1969 Robert Merton promoted continuous-time processes.

Merton was motivated by the desire to understand how prices are set in financial markets, the classical economics question of "equilibrium," and in papers he used the machinery of stochastic calculus to begin investigation of this issue. At the same time as Merton's work and with Merton's assistance, Fischer Black and Myron Scholes developed the Black–Scholes model, awarded the 1997 Nobel Memorial Prize in Economic Sciences, it provided a solution for a practical problem, that of finding a fair price for a European call option, i.e. the right to buy one share of a given stock at a specified price and time. Such options are purchased by investors as a risk-hedging device. In 1981, Harrison and Pliska used the general theory of continuous-time stochastic processes to put the Black–Scholes model on a solid theoretical basis, showed how to price numerous other derivative securities. Emanuel Derman's 2004 book My Life as a Quant helped to both make the role of a quantitative analyst better known outside of finance, to popularize the abbreviation "quant" for a quantitative analyst.

Quantitative analysts come from financial mathematics, financial engineering, applied mathematics, physics or engineering backgrounds, quantitative analysis is a major source of employment for people with mathematics and physics PhD degrees, or with financial mathematics master's degrees. A quantitative analyst will need extensive skills in computer programming, most C, C++, Java, R, MATLAB, Python; this demand for quantitative analysts has led to a resurgence in demand for actuarial qualifications as well as creation of specialized Masters and PhD courses in financial engineering, mathematical finance, computational finance, and/or financial reinsurance. In particular, Master's degrees in mathematical finance, financial engineering, operations research, computational statistics, machine learning, financial analysis are becoming more popular with students and with employers. See Master of Quantitative Finance. Data science and machine learning analysis and modelling methods are being employed in portfolio performance and portfolio risk modelling, as such data science and machine learning Master's graduates are in demand as quantitative analysts.

In sales and trading, quantitative analysts work to determine prices, manage risk, identify profitable opportunities. This was a distinct activity from trading but the boundary between a desk quantitative analyst and a quantitative trader is blurred, it is now difficult to enter trading as a profession without at least some quantitative analysis education. In the field of algorithmic trading it has reached the point where there is little meaningful difference. Front office work favours a higher speed to quality ratio, with a greater emphasis on solutions to specific problems than detailed modeling. FOQs are better paid than those in back office and model validation. Although skilled analysts, FOQs lack software engineering experience or formal training, bound by time constraints and business pressures, tactical solutions are adopted. Quantitative analysis is used extensively by asset managers. Some, such as FQ, AQR or Barclays, rely exclusively on quantitative strategies while others, such as Pimco, Blackrock or Citadel use a mix of quant

Isabelline shrike

The isabelline shrike or Daurian shrike is a member of the shrike family. It was considered conspecific with the red-backed shrike and red-tailed shrike, it is found in an extensive area between the Caspian Sea and north and central China southeast to the Qaidam Basin. Overwinters in Africa and Arabia; the genus name, Lanius, is derived from the Latin word for "butcher", some shrikes are known as "butcher birds" because of their feeding habits. The common name is from the specific isabellinus, New Latin for "greyish-yellow" named for Isabella I of Castile, said to have promised not to change her undergarments until Spain was freed from the Moors; the common English name "shrike" is from "shriek", referring to the shrill call. This migratory medium-sized passerine eats large insects, small birds and lizards. Like other shrikes it hunts from prominent perches, impales corpses on thorns or barbed wire as a larder, it breeds in open cultivated country, preferably with thorn bushes. The plumage is isabelline, the sandy colour.

It has a red tail. Young birds can be distinguished from young red-backed shrikes by the much sparser vermiculations on the underparts. Worfolk, Tim Identification of red-backed and brown shrikes Dutch Birding 22: 323-362 Pictures - Oiseaux

Total suspended solids

Total suspended solids is the dry-weight of suspended particles, that are not dissolved, in a sample of water that can be trapped by a filter, analyzed using a filtration apparatus. It is a water quality parameter used to assess the quality of a specimen of any type of water or water body, ocean water for example, or wastewater after treatment in a wastewater treatment plant, it is listed as a conventional pollutant in the U. S. Clean Water Act. Total dissolved solids is another parameter acquired through a separate analysis, used to determine water quality based on the total substances that are dissolved within the water, rather than undissolved suspended particles. TSS was called non-filterable residue, but was changed to TSS because of ambiguity in other scientific disciplines. TSS of a water or wastewater sample is determined by pouring a measured volume of water through a pre-weighed filter of a specified pore size weighing the filter again after the drying process that removes all water on the filter.

Filters for TSS measurements are composed of glass fibres. The gain in weight is a dry weight measure of the particulates present in the water sample expressed in units derived or calculated from the volume of water filtered. If the water contains an appreciable amount of dissolved substances, these will add to the weight of the filter as it is dried; therefore it is necessary to "wash" the filter and sample with deionized water after filtering the sample and before drying the filter. Failure to add this step is a common mistake made by inexperienced laboratory technicians working with sea water samples, will invalidate the results as the weight of salts left on the filter during drying can exceed that of the suspended particulate matter. Although turbidity purports to measure the same water quality property as TSS, the latter is more useful because it provides an actual weight of the particulate material present in the sample. In water quality monitoring situations, a series of more labor-intensive TSS measurements will be paired with quick and easy turbidity measurements to develop a site-specific correlation.

Once satisfactorily established, the correlation can be used to estimate TSS from more made turbidity measurements, saving time and effort. Because turbidity readings are somewhat dependent on particle size and color, this approach requires calculating a correlation equation for each location. Further, situations or conditions that tend to suspend larger particles through water motion can produce higher values of TSS not accompanied by a corresponding increase in turbidity; this is because particles above a certain size are not measured by a bench turbidity meter, but contribute to the TSS value. Although TSS appears to be a straightforward measure of particulate weight obtained by separating particles from a water sample using a filter, it suffers as a defined quantity from the fact that particles occur in nature in a continuum of sizes. At the lower end, TSS relies on a cut-off established by properties of the filter being used. At the upper end, the cut-off should be the exclusion of all particulates too large to be "suspended" in water.

However, this is not a fixed particle size but is dependent upon the energetics of the situation at the time of sampling: moving water suspends larger particles than does still water. It is the case that the additional suspended material caused by the movement of the water is of interest; these problems in no way invalidate the use of TSS. But comparisons between studies may require a careful review of the methodologies used to establish that the studies are in fact measuring the same thing. TSS in mg/L can be calculated as: / mL of sample * 1,000,000 Volatile suspended solids Bed load Settleable solids Turbidity – The cloudiness of a fluid caused by large numbers of particles that are invisible to the naked eye Water pollution – Contamination of water bodies Water quality