France recently pushed ahead of the European Union in implementing a financial transactions tax (FTT). Championed by both France and Germany, the European Union has been moving toward an FTT for several years, albeit with strong resistance from the United Kingdom. The new French FTT is fairly narrow in its base: 0.2 percent on the sale of stock of publicly-traded French companies valued above €1 billion (most FTT proposals would apply varying rates to range of assets—stocks, bonds, options, futures, and swaps—to minimize tax distortions and arbitrage opportunities). What’s unusual about France’s move is their additional high-frequency trading (HFT) tax, targeting algorithmic computer trades executed within half a second, as detailed by Steven Rosenthal on TaxVox.

The timing of France’s HFT tax is quite apropos given Knight Capital Group’s near-fatal $440 million trading loss from a software glitch triggering a wave of unintended trades (a cash lifeline from outside investors kept the firm afloat while severely diluting existing shares). Citing computer errors marring Facebook’s NASDAQ IPO, the Associated Press observed this week that, “Problems such as the one Knight caused last week have been occurring more regularly as the stock market’s trading systems come under increasing pressure from traders using huge computer systems.”

Indeed, remember the 2010 flash crash? In a bizarre spectacle on May 6 of that year, the Dow Jones Industrial Average—already down 4 percent for the day—abruptly plunged another 5-6 percent in a matter of minutes, hitting a floor down 992.6 points (-9.1 percent) from opening, and then rapidly rebounded. By the ring of the closing bell, the Chicago Board of Option Exchange’s Volatility Index for the S&P 500—a prime gauge of market fear—had surged 31.7 percent from the previous day’s close, the sixth-largest volatility spike this tumultuous decade. The Securities and Exchange Commission and the Commodities Futures Trading Commission later concluded the plunge was triggered when a “large fundamental trader” sold a batch of E-Mini S&P 500 futures contracts and S&P 500 SPDR exchange traded funds (two highly active financial instruments tracking stock indices) via an “automated execution algorithm (“Sell Algorithm”) that was programmed to feed orders into the June 2010 E-Mini market to target an execution rate set to 9 percent of the trading volume calculated over the previous minute, but without regard to price or time.”

So a shoddy computer program exerted massive downward price pressure without paying attention to price signals, triggering other automated algorithmic sell orders. Ten minutes wiped out $862 billion from securities’ market capitalization and some assets were buffeted to implausibly low valuations. The Focus Morningstar Healthcare ETF, for instance, briefly plunged to 60 cents from $25.33 per share and some trades were later canceled by regulators because market pricing was so distorted. This squares with which version of the efficient market hypothesis (EMH)? (Developed by economist Euguene Fama and associated with the freshwater Chicago School of economics, the EMH essentially states that market pricing efficiently reflects all public and in some cases non-public information.) Oops.

If you’re unmoved by the near-bankruptcy of a large equities market maker or whipsaw plunges on the Dow, watch this cool visualization of HFT volumes on U.S. stock exchanges surging since 2007, via Felix Salmon. Where’s the value added in this surge of high speed, algorithmic trading—trading that can trigger a flash liquidity crisis in the stock index futures market that spills into the real stock market? In the words of Paul Krugman: “The economic value of all this trading is dubious at best. In fact, there’s considerable evidence suggesting that too much trading is going on.” Throwing a little sand in the wheels would slow trading velocity, but wouldn’t adversely affect the societally valuable side of financial intermediation: directing funds from savers to borrowers looking to finance business expansion or the purchase of a home or education. (For a more thorough analysis of the efficiency improvements and the impact on productive investment, see this report by Dean Baker, who has been championing an FTT for years.)

A broad-based FTT would effectively shut down high speed automated trading, which is pure, unadulterated rent seeking by large financial firms—activity that adds systemic risk without adding value—but even as such, it would be a cash cow. A version proposed in the Congressional Progressive Caucus fiscal year 2013 Budget for All, based on an FTT scored by the Tax Policy Center, would raise a hefty $849 billion over the next decade—a comparable sum to allowing the upper-income Bush-era tax cuts to expire. Baker and Robert Pollin reckon an FTT could raise considerably more, in excess of $175 billion annually. And as my colleague Josh Bivens noted, it’s not just a way to raise revenue, it’s an exceptionally progressive, efficient way to raise revenue. For these reasons, support for an FTT has been growing not just abroad but also in the United States, with other variations having been proposed by Sen. Tom Harkin (D-Iowa) and Rep. Peter DeFazio (D-Oregon), as well as in comprehensive budget plans produced by the Center for American Progress, Roosevelt Institute Campus Network, and Our Fiscal Security—a partnership of Demos, EPI, and The Century Foundation.

The United States could take a lesson from the French—it’s time to raise some revenue and restore some sanity to financial markets run amok by lightly taxing financial transactions.