High Speed Equities Trading: 1993–2012

Abstract

High speed trading has drawn the attention of regulators who fear that such trading harms markets and leads to excessive speculation.1 The Flash Crash of May 6, 2010 is taken as evidence of the potential harmful effects of high frequency trading. On the other hand, some view high frequency trading as a manifestation of technological advances that have reduced the optimal trade size and improved order routing. From that perspective, high speed trading is a continuation of a long-standing trend to more rapid and more efficient trading. This study shows that the speed of trading has changed dramatically. The average number of trades per day for large cap New York Stock Exchange stocks has risen from about 500 to more than 40 000 in the period 1993-2011. At the same time, the average trade size has fallen from 1600 shares to 200 shares. The ultimate sources of these changes are the technology that has automated almost all aspects of trading and the regulatory developments that have helped reduce bid-ask spreads and made markets more accessible. The result of these developments is that markets are considerably more liquid and less costly. High frequency traders draw on this liquidity and also contribute to it.

Berbahayakah Instrumen Derivatif Dalam Konteks Akuntansi?

“…Metode critical terms merupakan pa ling mudah diterapkan untuk menilai efektifitas instrumen derivatif (Ramirez, 2013;Stoll, 2014). Metode ini pada dasarnya mempertimbangkan kesamaan beberapa hal yang terdapat pada transaksi 1 dan 2 pada Gambar 2, antara lain nilai nominal instrumen derivatif dengan nilai nominal transaksi/ item yang dilindung nilai, jangka waktu jatuh tempo instrumen derivatif de ngan jangka waktu transaksi/item yang dilin dung nilai, variabel dasar instrumen derivatif dengan transaksi/item yang dilindung nilai, nilai intrinsik instrumen derivatif harus bernilai nol pada saat awal kesepakatan.…”

The systemic role of technology in financial instability

“…At a micro-level, others propose risk-averse reinforcement learning for algorithmic trading (Shen, Huang et al 2014), thereby placing technological emphasis on the individual 'rationality' of distinct algorithmic players. This approach does not seem to be shared by other scholars who view the Flash Crash as a disastrous consequence of computerized trading systems (Edwards 2010), unlike Moosa (2015) who dismisses the criticism directed at highfrequency trading and Stoll (2014) who views such developments as side-effects towards a path of more liquid and less costly markets. In turn, this is contradicted by an examination of how news affects electronic systems, where Cai, Harris et al (2013) find that electronic markets are prone to greater stealth trading and post-trade volatility (expressing also concern for the proliferation of algorithmic trading and short-term volatility).…”

Is Informed Trading Different Across Investor Types?*

“…Figure 2 presents time‐series variation in the average trade size for individual investors, domestic institutional investors, and foreign institutional investors. It shows that the average trade size has decreased significantly during the sample period across all trader types, which is similar to the result from the U.S. stock markets (see, e.g., Angel et al ., 2011; Stoll, 2015). The decrease in trade size may be mainly attributable to the concurrent increase in high‐frequency trading in the Korean stock market.…”